stats.rs

static USAGE: &str = r#"
Compute summary statistics & infers data types for each column in a CSV.

> NOTE: `stats` is heavily optimized for speed. It assumes the CSV is well-formed and
UTF-8 encoded. If you encounter problems generating stats, use `qsv validate` to confirm the
input CSV is valid.

Summary stats include sum, min/max/range, sort order/sortiness, min/max/sum/avg/stddev/variance/cv length,
mean, standard error of the mean (SEM), geometric mean, harmonic mean, stddev, variance, coefficient of
variation (CV), nullcount, max_precision, sparsity, Median Absolute Deviation (MAD), quartiles,
interquartile range (IQR), lower/upper fences, skewness, median, cardinality/uniqueness ratio,
mode/s & "antimode/s" & percentiles.

Note that some stats require loading the entire file into memory, so they must be enabled explicitly. 

By default, the following "streaming" statistics are reported for *every* column:
  sum, min/max/range values, sort order/sortiness, min/max/sum/avg/stddev/variance/cv length, mean, sem,
  geometric_mean, harmonic_mean,stddev, variance, cv, nullcount, max_precision & sparsity.

The default set of statistics corresponds to ones that can be computed efficiently on a stream of data
(i.e., constant memory) and works with arbitrarily large CSVs.

The following additional "non-streaming" statistics require loading the entire file into memory:
cardinality/uniqueness ratio, modes/antimodes, median, MAD, quartiles and its related measures
(q1, q2, q3, IQR, lower/upper fences & skewness) and percentiles.

When computing "non-streaming" statistics, an Out-Of-Memory (OOM) heuristic check is done.
If the file is larger than the available memory minus a headroom buffer of 20% (which can be
adjusted using the QSV_FREEMEMORY_HEADROOM_PCT environment variable), processing will be
preemptively prevented.

"Antimode" is the least frequently occurring non-zero value and is the opposite of mode.
It returns "*ALL" if all the values are unique, and only returns a preview of the first
10 antimodes, truncating after 100 characters (configurable with QSV_ANTIMODES_LEN).

If you need all the antimode values of a column, run the `frequency` command with --limit set
to zero. The resulting frequency table will have all the "antimode" values.

Summary statistics for dates are also computed when --infer-dates is enabled, with DateTime
results in rfc3339 format and Date results in "yyyy-mm-dd" format in the UTC timezone.
Date range, stddev, variance, MAD & IQR are returned in days, not timestamp milliseconds.

Each column's data type is also inferred (NULL, Integer, String, Float, Date, DateTime and
Boolean with --infer-boolean option).
For String data types, it also determines if the column is all ASCII characters.
Unlike the sniff command, stats' data type inferences are GUARANTEED, as the entire file
is scanned, and not just sampled.

Note that the Date and DateTime data types are only inferred with the --infer-dates option 
as its an expensive operation to match a date candidate against 19 possible date formats,
with each format, having several variants.

The date formats recognized and its sub-variants along with examples can be found at 
https://github.com/dathere/qsv-dateparser?tab=readme-ov-file#accepted-date-formats.

Computing statistics on a large file can be made MUCH faster if you create an index for it
first with 'qsv index' to enable multithreading. With an index, the file is split into equal
chunks and each chunk is processed in parallel. The number of chunks is determined by the
number of logical CPUs detected. You can override this by setting the --jobs option.

As stats is a central command in qsv, and can be expensive to compute, `stats` caches results
in <FILESTEM>.stats.csv & if the --stats-json option is used, <FILESTEM>.stats.csv.data.jsonl
(e.g., qsv stats nyc311.csv will create nyc311.stats.csv & nyc311.stats.csv.data.jsonl).
The arguments used to generate the cached stats are saved in <FILESTEM>.stats.csv.jsonl.

If stats have already been computed for the input file with similar arguments and the file
hasn't changed, the stats will be loaded from the cache instead of recomputing it.

These cached stats are also used by other qsv commands (currently `describegpt`, `frequency`,
`joinp`, `pivotp`, `schema`, `sqlp` & `tojsonl`) to work smarter & faster.
If the cached stats are not current (i.e., the input file is newer than the cached stats),
the cached stats will be ignored and recomputed. For example, see the "boston311" test files in 
https://github.com/dathere/qsv/blob/4529d51273218347fef6aca15ac24e22b85b2ec4/tests/test_stats.rs#L608.

Examples:

Compute "streaming" statistics for the "nyc311.csv" file:
   $ qsv stats nyc311.csv

Compute all statistics for the "nyc311.csv" file:
    $ qsv stats --everything nyc311.csv
    $ qsv stats -E nyc311.csv

Compute all statistics for "nyc311.csv", inferring dates using default date column name patterns:
    $ qsv stats -E --infer-dates nyc311.csv

Compute all statistics for "nyc311.csv", inferring dates only for columns with "_date" & "_dte"
in the column names:
    $ qsv stats -E --infer-dates --dates-whitelist _date,_dte nyc311.csv

In addition, also infer boolean data types for "nyc311.csv" file:
    $ qsv stats -E --infer-dates --dates-whitelist _date --infer-boolean nyc311.csv

In addition to basic "streaming" stats, also compute cardinality for the "nyc311.csv" file:
    $ qsv stats --cardinality nyc311.csv

Prefer DMY format when inferring dates for the "nyc311.csv" file:
    $ qsv stats -E --infer-dates --prefer-dmy nyc311.csv    

Infer data types only for the "nyc311.csv" file:
    $ qsv stats --typesonly nyc311.csv

Infer data types only, including boolean and date types for the "nyc311.csv" file:
    $ qsv stats --typesonly --infer-boolean --infer-dates nyc311.csv

Automatically create an index for the "nyc311.csv" file to enable multithreading
if it's larger than 5MB and there is no existing index file:
    $ qsv stats -E --cache-threshold -5000000 nyc311.csv

Auto-create a TEMPORARY index for the "nyc311.csv" file to enable multithreading
if it's larger than 5MB and delete the index and the stats cache file after the stats run:
    $ qsv stats -E --cache-threshold -5000005 nyc311.csv

Prompt for CSV/TSV/TAB file to compute stats for:
    $ qsv prompt -F tsv,csv,tab | qsv stats -E | qsv table

Prompt for a file to save the stats to in the ~/Documents directory:
    $ qsv stats -E nyc311.csv | qsv prompt -d ~/Documents --fd-output

Prompt for both INPUT and OUTPUT files in the ~/Documents dir with custom prompts:
    $ qsv prompt -m 'Select a CSV file to summarize' -d ~/Documents -F csv | \
      qsv stats -E --infer-dates | \
      qsv prompt -m 'Save summary to...' -d ~/Documents --fd-output --save-fname summarystats.csv

For more examples, see https://github.com/dathere/qsv/tree/master/resources/test
For more info, see https://github.com/dathere/qsv/wiki/Supplemental#stats-command-output-explanation

Usage:
    qsv stats [options] [<input>]
    qsv stats --help

stats options:
    -s, --select <arg>        Select a subset of columns to compute stats for.
                              See 'qsv select --help' for the format details.
                              This is provided here because piping 'qsv select'
                              into 'qsv stats' will prevent the use of indexing.
    -E, --everything          Compute all statistics available EXCEPT --dataset-stats.
    --typesonly               Infer data types only and do not compute statistics.
                              Note that if you want to infer dates and boolean types, you'll
                              still need to use the --infer-dates & --infer-boolean options.

                              BOOLEAN INFERENCING:
    --infer-boolean           Infer boolean data type. This automatically enables
                              the --cardinality option. When a column's cardinality is 2,
                              and the 2 values' are in the true/false patterns specified
                              by --boolean-patterns, the data type is inferred as boolean.
    --boolean-patterns <arg>  Comma-separated list of boolean pattern pairs in the format
                              "true_pattern:false_pattern". Each pattern can be a string
                              of any length. The patterns are case-insensitive. If a pattern
                              ends with a "*", it is treated as a prefix. For example,
                              "t*:f*,y*:n*" will match "true", "truthy", "Truth" as boolean true
                              values so long as the corresponding false pattern (e.g. False, f, etc.)
                              is also matched & cardinality is 2. Ignored if --infer-boolean is false.
                              [default: 1:0,t*:f*,y*:n*]

    --mode                    Compute the mode/s & antimode/s. Multimodal-aware.
                              If there are multiple modes/antimodes, they are separated by the
                              QSV_STATS_SEPARATOR environment variable. If not set, the default
                              separator is "|".
                              Uses memory proportional to the cardinality of each column.
    --cardinality             Compute the cardinality and the uniqueness ratio.
                              This is automatically enabled if --infer-boolean is enabled.
                              https://en.wikipedia.org/wiki/Cardinality_(SQL_statements)
                              Uses memory proportional to the number of unique values in each column.

                              NUMERIC & DATE/DATETIME STATS THAT REQUIRE IN-MEMORY SORTING:
                              The following statistics are only computed for numeric & date/datetime
                              columns & require loading & sorting ALL the selected columns' data
                              in memory FIRST before computing the statistics.

    --median                  Compute the median.
                              Loads & sorts all the selected columns' data in memory.
                              https://en.wikipedia.org/wiki/Median
    --mad                     Compute the median absolute deviation (MAD).
                              https://en.wikipedia.org/wiki/Median_absolute_deviation
    --quartiles               Compute the quartiles (using method 3), the IQR, the lower/upper,
                              inner/outer fences and skewness.
                              https://en.wikipedia.org/wiki/Quartile#Method_3
    --percentiles             Compute custom percentiles using the nearest rank method.
                              https://en.wikipedia.org/wiki/Percentile#The_nearest-rank_method
    --percentile-list <arg>   Comma-separated list of percentiles to compute.
                              For example, "5,10,40,60,90,95" will compute percentiles
                              5th, 10th, 40th, 60th, 90th, and 95th.
                              Multiple percentiles are separated by the QSV_STATS_SEPARATOR
                              environment variable. If not set, the default separator is "|".
                              It is ignored if --percentiles is not set.
                              [default: 5,10,40,60,90,95]

    --round <decimal_places>  Round statistics to <decimal_places>. Rounding is done following
                              Midpoint Nearest Even (aka "Bankers Rounding") rule.
                              https://docs.rs/rust_decimal/latest/rust_decimal/enum.RoundingStrategy.html
                              If set to the sentinel value 9999, no rounding is done.
                              For dates - range, stddev & IQR are always at least 5 decimal places as
                              they are reported in days, and 5 places gives us millisecond precision.
                              [default: 4]
    --nulls                   Include NULLs in the population size for computing
                              mean and standard deviation.

                              DATE INFERENCING:
    --infer-dates             Infer date/datetime data types. This is an expensive
                              option and should only be used when you know there
                              are date/datetime fields.
                              Also, if timezone is not specified in the data, it'll
                              be set to UTC.
    --dates-whitelist <list>  The comma-separated, case-insensitive patterns to look for when 
                              shortlisting fields for date inferencing.
                              i.e. if the field's name has any of these patterns,
                              it is shortlisted for date inferencing.
                              Set to "all" to inspect ALL fields for
                              date/datetime types. Ignored if --infer-dates is false.

                              Note that false positive date matches WILL most likely occur
                              when using "all" as unix epoch timestamps are just numbers.
                              Be sure to only use "all" if you know ALL the columns you're
                              inspecting are dates, boolean or string fields.

                              To avoid false positives, preprocess the file first 
                              with the `datefmt` command to convert unix epoch timestamp
                              columns to RFC3339 format.
                              [default: date,time,due,open,close,created]
    --prefer-dmy              Parse dates in dmy format. Otherwise, use mdy format.
                              Ignored if --infer-dates is false.

    --force                   Force recomputing stats even if valid precomputed stats
                              cache exists.
    -j, --jobs <arg>          The number of jobs to run in parallel.
                              This works only when the given CSV has an index.
                              Note that a file handle is opened for each job.
                              When not set, the number of jobs is set to the
                              number of CPUs detected.
    --stats-jsonl             Also write the stats in JSONL format. 
                              If set, the stats will be written to <FILESTEM>.stats.csv.data.jsonl.
                              Note that this option used internally by other qsv "smart" commands (see
                              https://github.com/dathere/qsv/blob/master/docs/PERFORMANCE.md#stats-cache)
                              to load cached stats to make them work smarter & faster.
                              You can preemptively create the stats-jsonl file by using this option
                              BEFORE running "smart" commands and they will automatically use it.
 -c, --cache-threshold <arg>  Controls the creation of stats cache files.
                                - when greater than 1, the threshold in milliseconds before caching
                                  stats results. If a stats run takes longer than this threshold,
                                  the stats results will be cached.
                                - 0 to suppress caching. 
                                - 1 to force caching.
                                - a negative number to automatically create an index when
                                  the input file size is greater than abs(arg) in bytes.
                                  If the negative number ends with 5, it will delete the index
                                  file and the stats cache file after the stats run. Otherwise,
                                  the index file and the cache files are kept.
                              [default: 5000]
    --vis-whitespace          Visualize whitespace characters in the output.
                              See https://github.com/dathere/qsv/wiki/Supplemental#whitespace-markers
                              for the list of whitespace markers.
    --dataset-stats           Compute dataset statistics (e.g. row count, column count, file size and
                              fingerprint hash) and add them as additional rows to the output, with
                              the qsv__ prefix and an additional qsv__value column.
                              The --everything option DOES NOT enable this option.

Common options:
    -h, --help             Display this message
    -o, --output <file>    Write output to <file> instead of stdout.
    -n, --no-headers       When set, the first row will NOT be interpreted
                           as column names. i.e., They will be included
                           in statistics.
    -d, --delimiter <arg>  The field delimiter for READING CSV data.
                           Must be a single character. (default: ,)
    --memcheck             Check if there is enough memory to load the entire
                           CSV into memory using CONSERVATIVE heuristics.
                           This option is ignored when computing default, streaming
                           statistics, as it is not needed.
"#;

/*
DEVELOPER NOTE: stats is heavily optimized and is a central command in qsv.

It was the primary reason I created the qsv fork as I needed to do GUARANTEED data type
inferencing & to compile smart Data Dictionaries in the most performant way possible
for Datapusher+ (https://github.com/dathere/datapusher-plus).

It underpins the `schema` and `validate` commands - enabling the automatic creation of
a JSON Schema based on a CSV's summary statistics; and use the generated JSON Schema
to quickly validate complex CSVs hundreds of thousands of records/sec.

It's type inferences are also used by the "smart" commands (see
https://github.com/dathere/qsv/blob/master/docs/PERFORMANCE.md#stats-cache)
to make them work smarter & faster.

To safeguard against undefined behavior, `stats` is the most extensively tested command,
with ~520 tests.
*/

use std::{
    default::Default,
    fmt, fs, io,
    io::Write,
    iter::repeat_n,
    path::{Path, PathBuf},
    str,
    sync::OnceLock,
};

use crossbeam_channel;
use itertools::Itertools;
use phf::phf_map;
use qsv_dateparser::parse_with_preference;
use serde::{Deserialize, Serialize};
use simd_json::{OwnedValue, prelude::ValueAsScalar};
use smallvec::SmallVec;
use stats::{Commute, MinMax, OnlineStats, Unsorted, merge_all};
use tempfile::NamedTempFile;
use threadpool::ThreadPool;

use self::FieldType::{TDate, TDateTime, TFloat, TInteger, TNull, TString};
use crate::{
    CliResult,
    config::{Config, Delimiter, get_delim_by_extension},
    select::{SelectColumns, Selection},
    util,
};

#[allow(clippy::unsafe_derive_deserialize)]
#[derive(Clone, Deserialize)]
pub struct Args {
    pub arg_input:             Option<String>,
    pub flag_select:           SelectColumns,
    pub flag_everything:       bool,
    pub flag_typesonly:        bool,
    pub flag_infer_boolean:    bool,
    pub flag_boolean_patterns: String,
    pub flag_mode:             bool,
    pub flag_cardinality:      bool,
    pub flag_median:           bool,
    pub flag_mad:              bool,
    pub flag_quartiles:        bool,
    pub flag_percentiles:      bool,
    pub flag_percentile_list:  String,
    pub flag_round:            u32,
    pub flag_nulls:            bool,
    pub flag_infer_dates:      bool,
    pub flag_dates_whitelist:  String,
    pub flag_prefer_dmy:       bool,
    pub flag_force:            bool,
    pub flag_jobs:             Option<usize>,
    pub flag_stats_jsonl:      bool,
    pub flag_cache_threshold:  isize,
    pub flag_output:           Option<String>,
    pub flag_no_headers:       bool,
    pub flag_delimiter:        Option<Delimiter>,
    pub flag_memcheck:         bool,
    pub flag_vis_whitespace:   bool,
    pub flag_dataset_stats:    bool,
}

// this struct is used to serialize/deserialize the stats to
// the "".stats.csv.json" file which we check to see
// if we can skip recomputing stats.
#[derive(Clone, Serialize, Deserialize, PartialEq, Default)]
struct StatsArgs {
    arg_input:            String,
    flag_select:          String,
    flag_everything:      bool,
    flag_typesonly:       bool,
    flag_infer_boolean:   bool,
    flag_mode:            bool,
    flag_cardinality:     bool,
    flag_median:          bool,
    flag_mad:             bool,
    flag_quartiles:       bool,
    flag_percentiles:     bool,
    flag_percentile_list: String,
    flag_round:           u32,
    flag_nulls:           bool,
    flag_infer_dates:     bool,
    flag_dates_whitelist: String,
    flag_prefer_dmy:      bool,
    flag_no_headers:      bool,
    flag_delimiter:       String,
    flag_output_snappy:   bool,
    canonical_input_path: String,
    canonical_stats_path: String,
    record_count:         u64,
    date_generated:       String,
    compute_duration_ms:  u64,
    qsv_version:          String,
}

impl StatsArgs {
    // this is for deserializing the stats.csv.jsonl file
    fn from_owned_value(value: &OwnedValue) -> Result<Self, Box<dyn std::error::Error>> {
        Ok(Self {
            arg_input:            value["arg_input"].as_str().unwrap_or_default().to_string(),
            flag_select:          value["flag_select"]
                .as_str()
                .unwrap_or_default()
                .to_string(),
            flag_everything:      value["flag_everything"].as_bool().unwrap_or_default(),
            flag_typesonly:       value["flag_typesonly"].as_bool().unwrap_or_default(),
            flag_infer_boolean:   value["flag_infer_boolean"].as_bool().unwrap_or_default(),
            flag_mode:            value["flag_mode"].as_bool().unwrap_or_default(),
            flag_cardinality:     value["flag_cardinality"].as_bool().unwrap_or_default(),
            flag_median:          value["flag_median"].as_bool().unwrap_or_default(),
            flag_mad:             value["flag_mad"].as_bool().unwrap_or_default(),
            flag_quartiles:       value["flag_quartiles"].as_bool().unwrap_or_default(),
            flag_percentiles:     value["flag_percentiles"].as_bool().unwrap_or_default(),
            flag_percentile_list: value["flag_percentile_list"]
                .as_str()
                .unwrap_or("5,10,40,60,90,95")
                .to_string(),
            flag_round:           value["flag_round"].as_u64().unwrap_or_default() as u32,
            flag_nulls:           value["flag_nulls"].as_bool().unwrap_or_default(),
            flag_infer_dates:     value["flag_infer_dates"].as_bool().unwrap_or_default(),
            flag_dates_whitelist: value["flag_dates_whitelist"]
                .as_str()
                .unwrap_or_default()
                .to_string(),
            flag_prefer_dmy:      value["flag_prefer_dmy"].as_bool().unwrap_or_default(),
            flag_no_headers:      value["flag_no_headers"].as_bool().unwrap_or_default(),
            flag_delimiter:       value["flag_delimiter"]
                .as_str()
                .unwrap_or_default()
                .to_string(),
            flag_output_snappy:   value["flag_output_snappy"].as_bool().unwrap_or_default(),
            canonical_input_path: value["canonical_input_path"]
                .as_str()
                .unwrap_or_default()
                .to_string(),
            canonical_stats_path: value["canonical_stats_path"]
                .as_str()
                .unwrap_or_default()
                .to_string(),
            record_count:         value["record_count"].as_u64().unwrap_or_default(),
            date_generated:       value["date_generated"]
                .as_str()
                .unwrap_or_default()
                .to_string(),
            compute_duration_ms:  value["compute_duration_ms"].as_u64().unwrap_or_default(),
            qsv_version:          value["qsv_version"]
                .as_str()
                .unwrap_or_default()
                .to_string(),
        })
    }
}

#[derive(Clone, Serialize, Deserialize, PartialEq, Default, Debug)]
pub struct StatsData {
    pub field:                String,
    // type is a reserved keyword in Rust
    // so we escape it as r#type
    // we need to do this for serde to work
    pub r#type:               String,
    #[serde(default)]
    pub is_ascii:             bool,
    pub sum:                  Option<f64>,
    pub min:                  Option<String>,
    pub max:                  Option<String>,
    pub range:                Option<f64>,
    pub sort_order:           Option<String>,
    pub min_length:           Option<usize>,
    pub max_length:           Option<usize>,
    pub sum_length:           Option<usize>,
    pub avg_length:           Option<f64>,
    pub stddev_length:        Option<f64>,
    pub variance_length:      Option<f64>,
    pub cv_length:            Option<f64>,
    pub mean:                 Option<f64>,
    pub sem:                  Option<f64>,
    pub stddev:               Option<f64>,
    pub variance:             Option<f64>,
    pub cv:                   Option<f64>,
    pub nullcount:            u64,
    pub max_precision:        Option<u32>,
    pub sparsity:             Option<f64>,
    pub mad:                  Option<f64>,
    pub lower_outer_fence:    Option<f64>,
    pub lower_inner_fence:    Option<f64>,
    pub q1:                   Option<f64>,
    pub q2_median:            Option<f64>,
    pub q3:                   Option<f64>,
    pub iqr:                  Option<f64>,
    pub upper_inner_fence:    Option<f64>,
    pub upper_outer_fence:    Option<f64>,
    pub skewness:             Option<f64>,
    pub cardinality:          u64,
    pub uniqueness_ratio:     Option<f64>,
    pub mode:                 Option<String>,
    pub mode_count:           Option<u64>,
    pub mode_occurrences:     Option<u64>,
    pub antimode:             Option<String>,
    pub antimode_count:       Option<u64>,
    pub antimode_occurrences: Option<u64>,
}

#[derive(Clone, Serialize, Deserialize, PartialEq, Eq)]
pub enum JsonTypes {
    Int,
    Float,
    Bool,
    String,
}

// we use this to serialize the StatsData data structure
// to a JSONL file using serde_json
pub static STATSDATA_TYPES_MAP: phf::Map<&'static str, JsonTypes> = phf_map! {
    "field" => JsonTypes::String,
    "type" => JsonTypes::String,
    "is_ascii" => JsonTypes::Bool,
    "sum" => JsonTypes::Float,
    "min" => JsonTypes::String,
    "max" => JsonTypes::String,
    "range" => JsonTypes::Float,
    "sort_order" => JsonTypes::String,
    "sortiness" => JsonTypes::Float,
    "min_length" => JsonTypes::Int,
    "max_length" => JsonTypes::Int,
    "sum_length" => JsonTypes::Int,
    "avg_length" => JsonTypes::Float,
    "stddev_length" => JsonTypes::Float,
    "variance_length" => JsonTypes::Float,
    "cv_length" => JsonTypes::Float,
    "mean" => JsonTypes::Float,
    "sem" => JsonTypes::Float,
    "geometric_mean" => JsonTypes::Float,
    "harmonic_mean" => JsonTypes::Float,
    "stddev" => JsonTypes::Float,
    "variance" => JsonTypes::Float,
    "cv" => JsonTypes::Float,
    "nullcount" => JsonTypes::Int,
    "max_precision" => JsonTypes::Int,
    "sparsity" => JsonTypes::Float,
    "mad" => JsonTypes::Float,
    "lower_outer_fence" => JsonTypes::Float,
    "lower_inner_fence" => JsonTypes::Float,
    "q1" => JsonTypes::Float,
    "q2_median" => JsonTypes::Float,
    "q3" => JsonTypes::Float,
    "iqr" => JsonTypes::Float,
    "upper_inner_fence" => JsonTypes::Float,
    "upper_outer_fence" => JsonTypes::Float,
    "skewness" => JsonTypes::Float,
    "cardinality" => JsonTypes::Int,
    "uniqueness_ratio" => JsonTypes::Float,
    "mode" => JsonTypes::String,
    "mode_count" => JsonTypes::Int,
    "mode_occurrences" => JsonTypes::Int,
    "antimode" => JsonTypes::String,
    "antimode_count" => JsonTypes::Int,
    "antimode_occurrences" => JsonTypes::Int,
    "qsv__value" => JsonTypes::Int,
};

static INFER_DATE_FLAGS: OnceLock<SmallVec<[bool; 50]>> = OnceLock::new();
static RECORD_COUNT: OnceLock<u64> = OnceLock::new();
static ANTIMODES_LEN: OnceLock<usize> = OnceLock::new();

// standard overflow and underflow strings
// for sum, sum_length and avg_length
const OVERFLOW_STRING: &str = "*OVERFLOW*";
const UNDERFLOW_STRING: &str = "*UNDERFLOW*";

// number of milliseconds per day
const MS_IN_DAY: f64 = 86_400_000.0;
const MS_IN_DAY_INT: i64 = 86_400_000;
// number of decimal places when rounding days
// 5 decimal places give us millisecond precision
const DAY_DECIMAL_PLACES: u32 = 5;

// maximum number of output columns
const MAX_STAT_COLUMNS: usize = 44;

// the first N columns are fingerprint hash columns
const FINGERPRINT_HASH_COLUMNS: usize = 26;

// maximum number of antimodes to display
const MAX_ANTIMODES: usize = 10;
// default length of antimode string before truncating and appending "..."
const DEFAULT_ANTIMODES_LEN: usize = 100;

// the default separator we use for stats that have multiple values
// in one column, i.e. antimodes/modes & percentiles
pub const DEFAULT_STATS_SEPARATOR: &str = "|";

static BOOLEAN_PATTERNS: OnceLock<Vec<BooleanPattern>> = OnceLock::new();
#[derive(Clone, Debug)]
struct BooleanPattern {
    true_pattern:  String,
    false_pattern: String,
}

impl BooleanPattern {
    fn matches(&self, value: &str) -> Option<bool> {
        let value_lower = value.to_lowercase();

        // Check for exact match first
        if value_lower == self.true_pattern {
            return Some(true);
        } else if value_lower == self.false_pattern {
            return Some(false);
        }

        // Check for prefix match if pattern ends with "*"
        if self.true_pattern.ends_with('*') {
            let prefix = &self.true_pattern[..self.true_pattern.len() - 1];
            if value_lower.starts_with(prefix) {
                return Some(true);
            }
        }

        if self.false_pattern.ends_with('*') {
            let prefix = &self.false_pattern[..self.false_pattern.len() - 1];
            if value_lower.starts_with(prefix) {
                return Some(false);
            }
        }

        None
    }
}

fn parse_boolean_patterns(boolean_patterns: &str) -> CliResult<Vec<BooleanPattern>> {
    let mut patterns = Vec::new();
    for pair in boolean_patterns.split(',') {
        let mut parts = pair.split(':');
        let true_pattern = parts.next().unwrap_or("").trim().to_lowercase();
        let false_pattern = parts.next().unwrap_or("").trim().to_lowercase();

        if true_pattern.is_empty() || false_pattern.is_empty() {
            return fail_incorrectusage_clierror!("Invalid boolean pattern: {pair}");
        }

        patterns.push(BooleanPattern {
            true_pattern,
            false_pattern,
        });
    }
    if patterns.is_empty() {
        return fail_incorrectusage_clierror!("Boolean patterns must have at least one pattern");
    }
    Ok(patterns)
}

pub fn run(argv: &[&str]) -> CliResult<()> {
    let mut args: Args = util::get_args(USAGE, argv)?;
    if args.flag_typesonly {
        args.flag_everything = false;
        args.flag_mode = false;
        args.flag_cardinality = false;
        args.flag_median = false;
        args.flag_quartiles = false;
        args.flag_mad = false;
    }

    // inferring boolean requires inferring cardinality
    if args.flag_infer_boolean {
        if !args.flag_cardinality {
            args.flag_cardinality = true;
        }

        // validate boolean patterns
        let patterns = parse_boolean_patterns(&args.flag_boolean_patterns)?;
        let _ = BOOLEAN_PATTERNS.set(patterns);
    }

    // check prefer_dmy env var
    args.flag_prefer_dmy = args.flag_prefer_dmy || util::get_envvar_flag("QSV_PREFER_DMY");

    // set stdout output flag
    let stdout_output_flag = args.flag_output.is_none();

    // save the current args, we'll use it to generate
    // the stats.csv.json file
    let mut current_stats_args = StatsArgs {
        arg_input:            format!("{:?}", args.arg_input),
        flag_select:          format!("{:?}", args.flag_select),
        flag_everything:      args.flag_everything,
        flag_typesonly:       args.flag_typesonly,
        flag_infer_boolean:   args.flag_infer_boolean,
        flag_mode:            args.flag_mode,
        flag_cardinality:     args.flag_cardinality,
        flag_median:          args.flag_median,
        flag_mad:             args.flag_mad,
        flag_quartiles:       args.flag_quartiles,
        flag_percentiles:     args.flag_percentiles,
        flag_percentile_list: args.flag_percentile_list.clone(),
        flag_round:           args.flag_round,
        flag_nulls:           args.flag_nulls,
        flag_infer_dates:     args.flag_infer_dates,
        flag_dates_whitelist: args.flag_dates_whitelist.clone(),
        flag_prefer_dmy:      args.flag_prefer_dmy,
        flag_no_headers:      args.flag_no_headers,
        flag_delimiter:       format!("{:?}", args.flag_delimiter.clone()),
        // when we write to stdout, we don't use snappy compression
        // when we write to a file with the --output option, we use
        // snappy compression if the file ends with ".sz"
        flag_output_snappy:   if stdout_output_flag {
            false
        } else {
            let p = args.flag_output.clone().unwrap();
            p.to_ascii_lowercase().ends_with(".sz")
        },
        canonical_input_path: String::new(),
        canonical_stats_path: String::new(),
        record_count:         0,
        date_generated:       String::new(),
        compute_duration_ms:  0,
        // save the qsv version in the stats.csv.json file
        // so cached stats are automatically invalidated
        // when the qsv version changes
        qsv_version:          env!("CARGO_PKG_VERSION").to_string(),
    };

    // create a temporary file to store the <FILESTEM>.stats.csv file
    let stats_csv_tempfile = if current_stats_args.flag_output_snappy {
        tempfile::Builder::new().suffix(".sz").tempfile()?
    } else {
        NamedTempFile::new()?
    };

    // find the delimiter to use based on the extension of the output file
    // and if we need to snappy compress the output
    let (output_extension, output_delim, snappy) = match args.flag_output {
        Some(ref output_path) => get_delim_by_extension(Path::new(&output_path), b','),
        _ => (String::new(), b',', false),
    };
    let stats_csv_tempfile_fname = format!(
        "{stem}.{prime_ext}{snappy_ext}",
        //safety: we know the tempfile is a valid NamedTempFile, so we can use unwrap
        stem = stats_csv_tempfile.path().to_str().unwrap(),
        prime_ext = output_extension,
        snappy_ext = if snappy { ".sz" } else { "" }
    );

    // we will write the stats to a temp file
    let wconfig = Config::new(Some(stats_csv_tempfile_fname.clone()).as_ref())
        .delimiter(Some(Delimiter(output_delim)));
    let mut wtr = wconfig.writer()?;

    let mut rconfig = args.rconfig();
    if let Some(format_error) = rconfig.format_error {
        return fail_incorrectusage_clierror!("{format_error}");
    }
    let mut stdin_tempfile_path = None;

    if rconfig.is_stdin() {
        // read from stdin and write to a temp file
        log::info!("Reading from stdin");
        let mut stdin_file = NamedTempFile::new()?;
        let stdin = std::io::stdin();
        let mut stdin_handle = stdin.lock();
        std::io::copy(&mut stdin_handle, &mut stdin_file)?;
        drop(stdin_handle);
        let (_file, tempfile_path) = stdin_file
            .keep()
            .or(Err("Cannot keep temporary file".to_string()))?;
        stdin_tempfile_path = Some(tempfile_path.clone());
        args.arg_input = Some(tempfile_path.to_string_lossy().to_string());
        rconfig.path = Some(tempfile_path);
    } else {
        // check if the input file exists
        if let Some(path) = rconfig.path.clone() {
            if !path.exists() {
                return fail_clierror!("File {:?} does not exist", path.display());
            }
        }
    }

    let mut compute_stats = true;
    let mut create_cache = args.flag_cache_threshold == 1
        || args.flag_stats_jsonl
        || args.flag_cache_threshold.is_negative();

    let mut autoindex_set = false;

    let write_stats_jsonl = args.flag_stats_jsonl;

    if let Some(path) = rconfig.path.clone() {
        //safety: we know the path is a valid PathBuf, so we can use unwrap
        let path_file_stem = path.file_stem().unwrap().to_str().unwrap();
        let stats_file = stats_path(&path, false)?;
        // check if <FILESTEM>.stats.csv file already exists.
        // If it does, check if it was compiled using the same args.
        // However, if the --force flag is set,
        // recompute the stats even if the args are the same.
        if stats_file.exists() && !args.flag_force {
            let stats_args_json_file = stats_file.with_extension("csv.json");
            let existing_stats_args_json_str =
                match fs::read_to_string(stats_args_json_file.clone()) {
                    Ok(s) => s,
                    Err(e) => {
                        log::warn!(
                            "Could not read {path_file_stem}.stats.csv.json: {e:?}, recomputing..."
                        );
                        // remove stats cache files silently even if they don't exists
                        let _ = fs::remove_file(&stats_file);
                        let _ = fs::remove_file(&stats_args_json_file);
                        String::new()
                    },
                };

            if !existing_stats_args_json_str.is_empty() {
                let time_saved: u64;
                // deserialize the existing stats args json
                let existing_stats_args_json: StatsArgs = {
                    let mut json_buffer = existing_stats_args_json_str.into_bytes();
                    match simd_json::to_owned_value(&mut json_buffer) {
                        Ok(value) => {
                            // Convert OwnedValue to StatsArgs
                            match StatsArgs::from_owned_value(&value) {
                                Ok(mut stat_args) => {
                                    // we init these fields to empty values because we don't want to
                                    // compare them when checking if the
                                    // args are the same
                                    stat_args.canonical_input_path = String::new();
                                    stat_args.canonical_stats_path = String::new();
                                    stat_args.record_count = 0;
                                    stat_args.date_generated = String::new();
                                    time_saved = stat_args.compute_duration_ms;
                                    stat_args.compute_duration_ms = 0;
                                    stat_args
                                },
                                Err(e) => {
                                    time_saved = 0;
                                    log::warn!(
                                        "Could not deserialize {path_file_stem}.stats.csv.json: \
                                         {e:?}, recomputing..."
                                    );
                                    let _ = fs::remove_file(&stats_file);
                                    let _ = fs::remove_file(&stats_args_json_file);
                                    StatsArgs::default()
                                },
                            }
                        },
                        Err(e) => {
                            time_saved = 0;
                            log::warn!(
                                "Could not parse {path_file_stem}.stats.csv.json: {e:?}, \
                                 recomputing..."
                            );
                            let _ = fs::remove_file(&stats_file);
                            let _ = fs::remove_file(&stats_args_json_file);
                            StatsArgs::default()
                        },
                    }
                };

                // check if the cached stats are current (ie the stats file is newer than the input
                // file), use the same args or if the --everything flag was set, and
                // all the other non-stats args are equal. If so, we don't need to recompute the
                // stats
                let input_file_modified = fs::metadata(&path)?.modified()?;
                let stats_file_modified = fs::metadata(&stats_file)
                    .and_then(|m| m.modified())
                    .unwrap_or(input_file_modified);
                #[allow(clippy::nonminimal_bool)]
                if stats_file_modified > input_file_modified
                    && (existing_stats_args_json == current_stats_args
                        || existing_stats_args_json.flag_everything
                            && existing_stats_args_json.flag_infer_dates
                                == current_stats_args.flag_infer_dates
                            && existing_stats_args_json.flag_dates_whitelist
                                == current_stats_args.flag_dates_whitelist
                            && existing_stats_args_json.flag_prefer_dmy
                                == current_stats_args.flag_prefer_dmy
                            && existing_stats_args_json.flag_no_headers
                                == current_stats_args.flag_no_headers
                            && existing_stats_args_json.flag_dates_whitelist
                                == current_stats_args.flag_dates_whitelist
                            && existing_stats_args_json.flag_delimiter
                                == current_stats_args.flag_delimiter
                            && existing_stats_args_json.flag_nulls == current_stats_args.flag_nulls
                            && existing_stats_args_json.qsv_version
                                == current_stats_args.qsv_version)
                {
                    log::info!(
                        "{path_file_stem}.stats.csv already exists and is current. Skipping \
                         compute and using cached stats instead - {time_saved} milliseconds \
                         saved...",
                    );
                    compute_stats = false;
                } else {
                    log::info!(
                        "{path_file_stem}.stats.csv already exists, but is older than the input \
                         file or the args have changed, recomputing...",
                    );
                    let _ = fs::remove_file(&stats_file);
                }
            }
        }
        if compute_stats {
            let start_time = std::time::Instant::now();

            // we're loading the entire file into memory, we need to check avail mem
            if args.flag_everything
                || args.flag_mode
                || args.flag_cardinality
                || args.flag_median
                || args.flag_quartiles
                || args.flag_mad
            {
                util::mem_file_check(&path, false, args.flag_memcheck)?;
            }

            // check if flag_cache_threshold is a negative number,
            // if so, set the autoindex_size to absolute of the number
            if args.flag_cache_threshold.is_negative() {
                rconfig.autoindex_size = args.flag_cache_threshold.unsigned_abs() as u64;
                autoindex_set = true;
            }

            // we need to count the number of records in the file to calculate sparsity and
            // cardinality
            let record_count: u64;

            let (headers, stats) = match rconfig.indexed()? {
                None => {
                    // without an index, we need to count the number of records in the file
                    // safety: we know util::count_rows() will not return an Err
                    record_count = util::count_rows(&rconfig).unwrap();
                    args.sequential_stats(&args.flag_dates_whitelist)
                },
                Some(idx) => {
                    // with an index, we get the rowcount instantaneously from the index
                    record_count = idx.count();
                    match args.flag_jobs {
                        Some(num_jobs) => {
                            if num_jobs == 1 {
                                args.sequential_stats(&args.flag_dates_whitelist)
                            } else {
                                args.parallel_stats(&args.flag_dates_whitelist, record_count)
                            }
                        },
                        _ => args.parallel_stats(&args.flag_dates_whitelist, record_count),
                    }
                },
            }?;
            // we cache the record count so we don't have to count the records again
            let _ = RECORD_COUNT.set(record_count);
            // log::info!("scanned {record_count} records...");

            let stats_sr_vec = args.stats_to_records(stats, args.flag_vis_whitespace);
            let mut work_br;

            // vec we use to compute dataset-level fingerprint hash
            let mut stats_br_vec: Vec<csv::ByteRecord> = Vec::with_capacity(stats_sr_vec.len());

            let stats_headers_sr = args.stats_headers();
            wtr.write_record(&stats_headers_sr)?;
            let fields = headers.iter().zip(stats_sr_vec);
            for (i, (header, stat)) in fields.enumerate() {
                let header = if args.flag_no_headers {
                    i.to_string().into_bytes()
                } else {
                    header.to_vec()
                };
                let stat = stat.iter().map(str::as_bytes);
                work_br = vec![&*header]
                    .into_iter()
                    .chain(stat)
                    .collect::<csv::ByteRecord>();
                wtr.write_record(&work_br)?;
                stats_br_vec.push(work_br);
            }

            if args.flag_dataset_stats {
                // Add dataset-level stats as additional rows ====================
                let num_stats_fields = stats_headers_sr.len();
                let mut dataset_stats_br = csv::ByteRecord::with_capacity(128, num_stats_fields);

                // Helper closure to write a dataset stat row
                let mut write_dataset_stat = |name: &[u8], value: u64| -> CliResult<()> {
                    dataset_stats_br.clear();
                    dataset_stats_br.push_field(name);
                    // Fill middle columns with empty strings
                    for _ in 2..num_stats_fields {
                        dataset_stats_br.push_field(b"");
                    }
                    // write qsv__value as last column
                    dataset_stats_br.push_field(itoa::Buffer::new().format(value).as_bytes());
                    wtr.write_byte_record(&dataset_stats_br)
                        .map_err(std::convert::Into::into)
                };

                // Write qsv__rowcount
                write_dataset_stat(b"qsv__rowcount", record_count)?;

                // Write qsv__columncount
                let ds_column_count = headers.len() as u64;
                write_dataset_stat(b"qsv__columncount", ds_column_count)?;

                // Write qsv__filesize_bytes
                let ds_filesize_bytes = fs::metadata(&path)?.len();
                write_dataset_stat(b"qsv__filesize_bytes", ds_filesize_bytes)?;

                // Compute hash of stats for data fingerprinting
                let stats_hash = {
                    // the first FINGERPRINT_HASH_COLUMNS are used for the fingerprint hash
                    let mut hash_input = Vec::with_capacity(FINGERPRINT_HASH_COLUMNS);

                    // First, create a stable representation of the stats
                    for record in &stats_br_vec {
                        // Take FINGERPRINT_HASH_COLUMNS columns only
                        for field in record.iter().take(FINGERPRINT_HASH_COLUMNS) {
                            let s = String::from_utf8_lossy(field);
                            // Standardize number format
                            if let Ok(f) = s.parse::<f64>() {
                                hash_input.extend_from_slice(format!("{f:.10}").as_bytes());
                            } else {
                                hash_input.extend_from_slice(field);
                            }
                            hash_input.push(0x1F); // field separator
                        }
                        hash_input.push(b'\n');
                    }

                    // Add dataset stats
                    hash_input.extend_from_slice(
                        format!("{record_count}\x1F{ds_column_count}\x1F{ds_filesize_bytes}\n")
                            .as_bytes(),
                    );
                    sha256::digest(hash_input.as_slice())
                };

                dataset_stats_br.clear();
                dataset_stats_br.push_field(b"qsv__fingerprint_hash");
                // Fill middle columns with empty strings
                for _ in 2..num_stats_fields {
                    dataset_stats_br.push_field(b"");
                }
                // write qsv__value as last column
                dataset_stats_br.push_field(stats_hash.as_bytes());
                wtr.write_byte_record(&dataset_stats_br)?;
            }

            // update the stats args json metadata ===============
            // if the stats run took longer than the cache threshold and the threshold > 0,
            // cache the stats so we don't have to recompute it next time
            current_stats_args.compute_duration_ms = start_time.elapsed().as_millis() as u64;
            create_cache = create_cache
                || current_stats_args.compute_duration_ms > args.flag_cache_threshold as u64;

            // only init these info if we're creating a stats cache
            if create_cache {
                // safety: we know the path is a valid PathBuf, so we can use unwrap
                current_stats_args.canonical_input_path =
                    path.canonicalize()?.to_str().unwrap().to_string();
                current_stats_args.record_count = record_count;
                current_stats_args.date_generated = chrono::Utc::now().to_rfc3339();
            }
        }
    }

    wtr.flush()?;

    if let Some(pb) = stdin_tempfile_path {
        // remove the temp file we created to store stdin
        std::fs::remove_file(pb)?;
    }

    let currstats_filename = if compute_stats {
        // we computed the stats, use the stats temp file
        stats_csv_tempfile_fname
    } else {
        // we didn't compute the stats, re-use the existing stats file
        // safety: we know the path is a valid PathBuf, so we can use unwrap
        stats_path(rconfig.path.as_ref().unwrap(), false)?
            .to_str()
            .unwrap()
            .to_owned()
    };

    if rconfig.is_stdin() {
        // if we read from stdin, copy the temp stats file to "stdin.stats.csv"
        // safety: we know the path is a valid PathBuf, so we can use unwrap
        let mut stats_pathbuf = stats_path(rconfig.path.as_ref().unwrap(), true)?;
        fs::copy(currstats_filename.clone(), stats_pathbuf.clone())?;

        // save the stats args to "stdin.stats.csv.json"
        stats_pathbuf.set_extension("csv.json");
        std::fs::write(
            stats_pathbuf,
            serde_json::to_string_pretty(&current_stats_args)?,
        )?;
    } else if let Some(path) = rconfig.path {
        // if we read from a file, copy the temp stats file to "<FILESTEM>.stats.csv"
        let mut stats_pathbuf = path.clone();
        stats_pathbuf.set_extension("stats.csv");
        // safety: we know the path is a valid PathBuf, so we can use unwrap
        if currstats_filename != stats_pathbuf.to_str().unwrap() {
            // if the stats file is not the same as the input file, copy it
            fs::copy(currstats_filename.clone(), stats_pathbuf.clone())?;
        }

        if args.flag_cache_threshold == 0
            || (args.flag_cache_threshold.is_negative() && args.flag_cache_threshold % 10 == -5)
        {
            // if the cache threshold zero or is a negative number ending in 5,
            // delete both the index file and the stats cache file
            if autoindex_set {
                let index_file = path.with_extension("csv.idx");
                log::debug!("deleting index file: {}", index_file.display());
                if std::fs::remove_file(index_file.clone()).is_err() {
                    // fails silently if it can't remove the index file
                    log::warn!("Could not remove index file: {}", index_file.display());
                }
            }

            // remove the stats cache file
            if fs::remove_file(stats_pathbuf.clone()).is_err() {
                // fails silently if it can't remove the stats file
                log::warn!(
                    "Could not remove stats cache file: {}",
                    stats_pathbuf.display()
                );
            }
            create_cache = false;
        }

        if compute_stats && create_cache {
            // save the stats args to "<FILESTEM>.stats.csv.json"
            // if we computed the stats
            stats_pathbuf.set_extension("csv.json");
            // write empty file first so we can canonicalize it
            std::fs::File::create(stats_pathbuf.clone())?;
            // safety: we know the path is a valid PathBuf, so we can use unwrap
            current_stats_args.canonical_stats_path = stats_pathbuf
                .clone()
                .canonicalize()?
                .to_str()
                .unwrap()
                .to_string();
            std::fs::write(
                stats_pathbuf.clone(),
                serde_json::to_string_pretty(&current_stats_args)?,
            )?;

            // save the stats data to "<FILESTEM>.stats.csv.data.jsonl"
            if write_stats_jsonl {
                let mut stats_jsonl_pathbuf = stats_pathbuf.clone();
                stats_jsonl_pathbuf.set_extension("data.jsonl");
                util::csv_to_jsonl(
                    &currstats_filename,
                    &STATSDATA_TYPES_MAP,
                    &stats_jsonl_pathbuf,
                )?;
            }
        }
    }

    if stdout_output_flag {
        // if we're outputting to stdout, copy the stats file to stdout
        let currstats = fs::read_to_string(currstats_filename)?;
        io::stdout().write_all(currstats.as_bytes())?;
        io::stdout().flush()?;
    } else if let Some(output) = args.flag_output {
        // if we're outputting to a file, copy the stats file to the output file
        if currstats_filename != output {
            // if the stats file is not the same as the output file, copy it
            fs::copy(currstats_filename, output)?;
        }
    }

    Ok(())
}

impl Args {
    fn sequential_stats(&self, whitelist: &str) -> CliResult<(csv::ByteRecord, Vec<Stats>)> {
        let mut rdr = self.rconfig().reader()?;
        let (headers, sel) = self.sel_headers(&mut rdr)?;

        init_date_inference(self.flag_infer_dates, &headers, whitelist)?;

        let stats = self.compute(&sel, rdr.byte_records());
        Ok((headers, stats))
    }

    fn parallel_stats(
        &self,
        whitelist: &str,
        idx_count: u64,
    ) -> CliResult<(csv::ByteRecord, Vec<Stats>)> {
        // N.B. This method doesn't handle the case when the number of records
        // is zero correctly. So we use `sequential_stats` instead.
        if idx_count == 0 {
            return self.sequential_stats(whitelist);
        }

        let mut rdr = self.rconfig().reader()?;
        let (headers, sel) = self.sel_headers(&mut rdr)?;

        init_date_inference(self.flag_infer_dates, &headers, whitelist)?;

        let njobs = util::njobs(self.flag_jobs);
        let chunk_size = util::chunk_size(idx_count as usize, njobs);
        let nchunks = util::num_of_chunks(idx_count as usize, chunk_size);

        let pool = ThreadPool::new(njobs);
        let (send, recv) = crossbeam_channel::bounded(nchunks);
        for i in 0..nchunks {
            let (send, args, sel) = (send.clone(), self.clone(), sel.clone());
            pool.execute(move || {
                // safety: indexed() & seek() are safe as we know we have an index file
                // if indexed() or seek() does return an Err, you have a bigger problem
                // as the index file was modified WHILE stats is running and you actually
                // NEED to abort if that happens, however unlikely
                let mut idx = unsafe {
                    args.rconfig()
                        .indexed()
                        .unwrap_unchecked()
                        .unwrap_unchecked()
                };
                idx.seek((i * chunk_size) as u64)
                    .expect("File seek failed.");
                let it = idx.byte_records().take(chunk_size);
                // safety: this will only return an Error if the channel has been disconnected
                unsafe {
                    send.send(args.compute(&sel, it)).unwrap_unchecked();
                }
            });
        }
        drop(send);
        Ok((headers, merge_all(recv.iter()).unwrap_or_default()))
    }

    fn stats_to_records(&self, stats: Vec<Stats>, visualize_ws: bool) -> Vec<csv::StringRecord> {
        let round_places = self.flag_round;
        let infer_boolean = self.flag_infer_boolean;
        let dataset_stats = self.flag_dataset_stats;
        let mut records = Vec::with_capacity(stats.len());
        records.extend(repeat_n(csv::StringRecord::new(), stats.len()));
        let pool = ThreadPool::new(util::njobs(self.flag_jobs));
        let mut results = Vec::with_capacity(stats.len());
        for mut stat in stats {
            let (send, recv) = crossbeam_channel::bounded(0);
            results.push(recv);
            pool.execute(move || {
                // safety: this will only return an Error if the channel has been disconnected
                // which will not happen in this case
                send.send(stat.to_record(round_places, infer_boolean, visualize_ws, dataset_stats))
                    .unwrap();
            });
        }
        for (i, recv) in results.into_iter().enumerate() {
            // safety: results.len() == records.len() so we know the index is valid
            // and doesn't require a bounds check.
            // The unwrap on recv.recv() is safe as the channel is bounded
            unsafe {
                *records.get_unchecked_mut(i) = recv.recv().unwrap();
            }
        }
        records
    }

    #[inline]
    fn compute<I>(&self, sel: &Selection, it: I) -> Vec<Stats>
    where
        I: Iterator<Item = csv::Result<csv::ByteRecord>>,
    {
        let sel_len = sel.len();
        let mut stats = self.new_stats(sel_len);

        // safety: we know INFER_DATE_FLAGS is Some because we called init_date_inference
        let infer_date_flags = INFER_DATE_FLAGS.get().unwrap();

        // so we don't need to get infer_boolean/prefer_dmy from big args struct for each iteration
        // and hopefully the compiler will optimize this and use registers in the hot loop
        let infer_boolean = self.flag_infer_boolean;
        let prefer_dmy = self.flag_prefer_dmy;

        let mut i;
        #[allow(unused_assignments)]
        let mut current_row = csv::ByteRecord::with_capacity(1024, sel_len);
        for row in it {
            i = 0;
            // safety: because we're using iterators and INFER_DATE_FLAGS has the same size,
            // we know we don't need to bounds check
            unsafe {
                current_row = row.unwrap_unchecked();
                for field in sel.select(&current_row) {
                    stats.get_unchecked_mut(i).add(
                        field,
                        *infer_date_flags.get_unchecked(i),
                        infer_boolean,
                        prefer_dmy,
                    );
                    i += 1;
                }
            }
        }
        stats
    }

    #[inline]
    fn sel_headers<R: io::Read>(
        &self,
        rdr: &mut csv::Reader<R>,
    ) -> CliResult<(csv::ByteRecord, Selection)> {
        let headers = rdr.byte_headers()?.clone();
        let sel = self.rconfig().selection(&headers)?;
        Ok((sel.select(&headers).collect(), sel))
    }

    #[inline]
    fn rconfig(&self) -> Config {
        Config::new(self.arg_input.as_ref())
            .delimiter(self.flag_delimiter)
            .no_headers(self.flag_no_headers)
            .select(self.flag_select.clone())
    }

    #[inline]
    fn new_stats(&self, record_len: usize) -> Vec<Stats> {
        let mut stats: Vec<Stats> = Vec::with_capacity(record_len);
        stats.extend(repeat_n(
            Stats::new(WhichStats {
                include_nulls:   self.flag_nulls,
                sum:             !self.flag_typesonly,
                range:           !self.flag_typesonly || self.flag_infer_boolean,
                dist:            !self.flag_typesonly,
                cardinality:     self.flag_everything || self.flag_cardinality,
                median:          !self.flag_everything && self.flag_median && !self.flag_quartiles,
                mad:             self.flag_everything || self.flag_mad,
                quartiles:       self.flag_everything || self.flag_quartiles,
                mode:            self.flag_everything || self.flag_mode,
                typesonly:       self.flag_typesonly,
                percentiles:     self.flag_everything || self.flag_percentiles,
                percentile_list: self.flag_percentile_list.clone(),
            }),
            record_len,
        ));
        stats
    }

    pub fn stats_headers(&self) -> csv::StringRecord {
        if self.flag_typesonly {
            return csv::StringRecord::from(vec!["field", "type"]);
        }

        // with --everything, we have MAX_STAT_COLUMNS columns at most
        let mut fields = Vec::with_capacity(MAX_STAT_COLUMNS);

        // these are the standard stats columns that are always output
        // the "streaming" stats that are always included in stats output
        // aka the FINGERPRINT_HASH_COLUMNS
        fields.extend_from_slice(&[
            "field",
            "type",
            "is_ascii",
            "sum",
            "min",
            "max",
            "range",
            "sort_order",
            "sortiness",
            "min_length",
            "max_length",
            "sum_length",
            "avg_length",
            "stddev_length",
            "variance_length",
            "cv_length",
            "mean",
            "sem",
            "geometric_mean",
            "harmonic_mean",
            "stddev",
            "variance",
            "cv",
            "nullcount",
            "max_precision",
            "sparsity",
        ]);

        // these are the stats columns that are only output if the user requested them
        let everything = self.flag_everything;
        if self.flag_median && !self.flag_quartiles && !everything {
            fields.push("median");
        }
        if self.flag_mad || everything {
            fields.push("mad");
        }
        if self.flag_quartiles || everything {
            fields.extend_from_slice(&[
                "lower_outer_fence",
                "lower_inner_fence",
                "q1",
                "q2_median",
                "q3",
                "iqr",
                "upper_inner_fence",
                "upper_outer_fence",
                "skewness",
            ]);
        }
        if self.flag_cardinality || everything {
            fields.extend_from_slice(&["cardinality", "uniqueness_ratio"]);
        }
        if self.flag_mode || everything {
            fields.extend_from_slice(&[
                "mode",
                "mode_count",
                "mode_occurrences",
                "antimode",
                "antimode_count",
                "antimode_occurrences",
            ]);
        }
        if self.flag_percentiles || everything {
            fields.push("percentiles");
        }
        if self.flag_dataset_stats {
            // we add the qsv__value field at the end for dataset-level stats
            fields.push("qsv__value");
        }

        csv::StringRecord::from(fields)
    }
}

/// returns the path to the stats file
fn stats_path(stats_csv_path: &Path, stdin_flag: bool) -> io::Result<PathBuf> {
    let parent = stats_csv_path
        .parent()
        .ok_or_else(|| io::Error::new(io::ErrorKind::InvalidInput, "Invalid path"))?;
    let fstem = stats_csv_path
        .file_stem()
        .ok_or_else(|| io::Error::new(io::ErrorKind::InvalidInput, "Invalid file name"))?;

    let new_fname = if stdin_flag {
        "stdin.stats.csv".to_string()
    } else {
        format!("{}.stats.csv", fstem.to_string_lossy())
    };

    Ok(parent.join(new_fname))
}

fn init_date_inference(
    infer_dates: bool,
    headers: &csv::ByteRecord,
    flag_whitelist: &str,
) -> Result<(), String> {
    if !infer_dates {
        // we're not inferring dates, set INFER_DATE_FLAGS to all false
        INFER_DATE_FLAGS
            .set(SmallVec::from_elem(false, headers.len()))
            .map_err(|_| "Cannot init empty date inference flags".to_string())?;
        return Ok(());
    }

    let infer_date_flags = if flag_whitelist.eq_ignore_ascii_case("all") {
        log::info!("inferring dates for ALL fields");
        SmallVec::from_elem(true, headers.len())
    } else {
        let mut header_str = String::new();
        let whitelist_lower = flag_whitelist.to_lowercase();
        log::info!("inferring dates with date-whitelist: {whitelist_lower}");

        let whitelist: SmallVec<[&str; 8]> = whitelist_lower.split(',').map(str::trim).collect();
        headers
            .iter()
            .map(|header| {
                util::to_lowercase_into(
                    simdutf8::basic::from_utf8(header).unwrap_or_default(),
                    &mut header_str,
                );
                whitelist
                    .iter()
                    .any(|whitelist_item| header_str.contains(whitelist_item))
            })
            .collect()
    };

    INFER_DATE_FLAGS
        .set(infer_date_flags)
        .map_err(|e| format!("Cannot init date inference flags: {e:?}"))?;
    Ok(())
}

#[derive(Clone, Debug, Eq, PartialEq, Default, Serialize, Deserialize)]
struct WhichStats {
    include_nulls:   bool,
    sum:             bool,
    range:           bool,
    dist:            bool,
    cardinality:     bool,
    median:          bool,
    mad:             bool,
    quartiles:       bool,
    mode:            bool,
    typesonly:       bool,
    percentiles:     bool,
    percentile_list: String,
}

impl Commute for WhichStats {
    #[inline]
    fn merge(&mut self, other: WhichStats) {
        assert_eq!(*self, other);
    }
}

#[allow(clippy::unsafe_derive_deserialize)]
#[repr(C)]
#[derive(Clone, Serialize, Deserialize, PartialEq)]
struct Stats {
    // optimal memory layout for this central struct
    // this ordering consumes 688 bytes
    typ:            FieldType,                 // 1 byte
    is_ascii:       bool,                      // 1 byte
    max_precision:  u16,                       // 2 bytes
    which:          WhichStats,                // 10 bytes
    nullcount:      u64,                       // 8 bytes
    sum_stotlen:    u64,                       // 8 bytes
    sum:            Option<TypedSum>,          // 32 bytes
    modes:          Option<Unsorted<Vec<u8>>>, // 32 bytes
    // we use the same Unsorted struct for median, mad, quartiles & percentiles
    #[allow(clippy::struct_field_names)]
    unsorted_stats: Option<Unsorted<f64>>, // 32 bytes
    online:         Option<OnlineStats>, // 48 bytes
    online_len:     Option<OnlineStats>, // 48 bytes
    minmax:         Option<TypedMinMax>, // 432 bytes
}

#[inline]
fn timestamp_ms_to_rfc3339(timestamp: i64, typ: FieldType) -> String {
    let date_val = chrono::DateTime::from_timestamp_millis(timestamp)
        .unwrap_or_default()
        .to_rfc3339();

    // if type = Date, only return the date component
    // do not return the time component
    if typ == TDate {
        return date_val[..10].to_string();
    }
    date_val
}

impl Stats {
    fn new(which: WhichStats) -> Stats {
        let (mut sum, mut minmax, mut online, mut online_len, mut modes, mut unsorted_stats) =
            (None, None, None, None, None, None);
        if which.sum {
            sum = Some(TypedSum::default());
        }
        if which.range {
            minmax = Some(TypedMinMax::default());
        }
        if which.dist {
            online = Some(stats::OnlineStats::default());
            online_len = Some(stats::OnlineStats::default());
        }
        if which.mode || which.cardinality {
            modes = Some(stats::Unsorted::default());
        }
        // we use the same Unsorted struct for median, mad, quartiles & percentiles
        if which.quartiles || which.median || which.mad || which.percentiles {
            unsorted_stats = Some(stats::Unsorted::default());
        }
        Stats {
            typ: FieldType::default(),
            is_ascii: true,
            max_precision: 0,
            which,
            nullcount: 0,
            sum_stotlen: 0,
            sum,
            modes,
            unsorted_stats,
            online,
            online_len,
            minmax,
        }
    }

    #[allow(clippy::inline_always)]
    #[inline(always)]
    fn add(&mut self, sample: &[u8], infer_dates: bool, infer_boolean: bool, prefer_dmy: bool) {
        let (sample_type, timestamp_val) =
            FieldType::from_sample(infer_dates, prefer_dmy, sample, self.typ);
        self.typ.merge(sample_type);

        // we're inferring --typesonly, so don't add samples to compute statistics
        // unless we need to --infer-boolean. In which case, we need --cardinality
        // and --range, so we need to add samples.
        if self.which.typesonly && !infer_boolean {
            return;
        }

        let t = self.typ;
        if let Some(v) = self.sum.as_mut() {
            v.add(t, sample);
        }
        if let Some(v) = self.minmax.as_mut() {
            if timestamp_val == 0 {
                v.add(t, sample);
            } else {
                v.add(t, itoa::Buffer::new().format(timestamp_val).as_bytes());
            }
        }
        if let Some(v) = self.modes.as_mut() {
            v.add(sample.to_vec());
        }
        if sample_type == TNull {
            self.nullcount += 1;
        }
        match t {
            TString => {
                // only check if the string is_ascii while it still is
                // once it's false, don't needlessly call is_ascii()
                if self.is_ascii {
                    self.is_ascii = sample.is_ascii();
                }
                if let Some(v) = self.online_len.as_mut() {
                    v.add(&sample.len());
                }
            },
            TFloat | TInteger => {
                if sample_type == TNull {
                    if self.which.include_nulls {
                        if let Some(v) = self.online.as_mut() {
                            v.add_null();
                        }
                    }
                } else {
                    // safety: we know the sample is a valid f64, so we can use unwrap
                    let n = unsafe { fast_float2::parse(sample).unwrap_unchecked() };
                    if let Some(v) = self.unsorted_stats.as_mut() {
                        v.add(n);
                    }
                    if let Some(v) = self.online.as_mut() {
                        v.add(&n);
                    }
                    if t == TFloat {
                        let mut ryu_buffer = ryu::Buffer::new();
                        // safety: we know that n is a valid f64
                        // so there will always be a fraction part, even if it's 0
                        let fractpart = unsafe {
                            ryu_buffer
                                .format_finite(n)
                                .split('.')
                                .next_back()
                                .unwrap_unchecked()
                        };
                        self.max_precision = std::cmp::max(
                            self.max_precision,
                            (if *fractpart == *"0" {
                                0
                            } else {
                                fractpart.len()
                            }) as u16,
                        );
                    }
                }
            },
            TNull => {
                if self.which.include_nulls {
                    if let Some(v) = self.online.as_mut() {
                        v.add_null();
                    }
                }
            },
            TDateTime | TDate => {
                if sample_type == TNull {
                    if self.which.include_nulls {
                        if let Some(v) = self.online.as_mut() {
                            v.add_null();
                        }
                    }
                // if timestamp_val != 0, then we successfully inferred a date from the sample
                } else if timestamp_val != 0 {
                    // calculate date statistics by adding date samples as timestamps to
                    // millisecond precision.
                    #[allow(clippy::cast_precision_loss)]
                    let n = timestamp_val as f64;
                    if let Some(v) = self.unsorted_stats.as_mut() {
                        v.add(n);
                    }
                    if let Some(v) = self.online.as_mut() {
                        v.add(&n);
                    }
                }
            },
        }
    }

    #[allow(clippy::wrong_self_convention)]
    pub fn to_record(
        &mut self,
        round_places: u32,
        infer_boolean: bool,
        visualize_ws: bool,
        dataset_stats: bool,
    ) -> csv::StringRecord {
        // we're doing typesonly and not inferring boolean, just return the type
        if self.which.typesonly && !infer_boolean {
            return csv::StringRecord::from(vec![self.typ.to_string()]);
        }

        let typ = self.typ;
        // prealloc memory for performance
        // we have MAX_STAT_COLUMNS columns at most with --everything
        let mut pieces = Vec::with_capacity(MAX_STAT_COLUMNS);

        let empty = String::new;

        // min/max/range/sort_order/sortiness
        // we do this first as we want to get the sort_order, so we can skip sorting if not
        // required. We also need to do this before --infer-boolean because we need to know
        // the min/max values to determine if the range is equal to the supported boolean
        // ranges as specified by --boolean-patterns.
        let mut minmax_range_sortorder_pieces = Vec::with_capacity(5);
        let mut minval = String::new();
        let mut maxval = String::new();
        let mut column_sorted = false;
        if let Some(mm) = self
            .minmax
            .as_ref()
            .and_then(|mm| mm.show(typ, round_places, visualize_ws))
        {
            // save min/max values for boolean inferencing
            minval.clone_from(&mm.0);
            maxval.clone_from(&mm.1);
            if mm.3.starts_with("Ascending") {
                column_sorted = true;
            }
            minmax_range_sortorder_pieces.extend_from_slice(&[mm.0, mm.1, mm.2, mm.3, mm.4]);
        } else {
            minmax_range_sortorder_pieces.extend_from_slice(&[
                empty(),
                empty(),
                empty(),
                empty(),
                empty(),
            ]);
        }

        let record_count = *RECORD_COUNT.get().unwrap_or(&1);

        // get the stats separator
        let stats_separator = if self.which.mode || self.which.percentiles {
            std::env::var("QSV_STATS_SEPARATOR")
                .unwrap_or_else(|_| DEFAULT_STATS_SEPARATOR.to_string())
        } else {
            DEFAULT_STATS_SEPARATOR.to_string()
        };

        // modes/antimodes & cardinality/uniqueness_ratio
        // we do this second because we can use the sort order with cardinality, to skip sorting
        // if its not required. This makes not only cardinality computation faster, it also makes
        // modes/antimodes computation faster.
        // We also need to know the cardinality to --infer-boolean should that be enabled
        let mut cardinality = 0;
        let mut mc_pieces = Vec::with_capacity(8);
        match self.modes.as_mut() {
            None => {
                if self.which.cardinality {
                    mc_pieces.extend_from_slice(&[empty(), empty()]);
                }
                if self.which.mode {
                    mc_pieces.extend_from_slice(&[
                        empty(),
                        empty(),
                        empty(),
                        empty(),
                        empty(),
                        empty(),
                    ]);
                }
            },
            Some(ref mut v) => {
                if self.which.cardinality {
                    cardinality = v.cardinality(column_sorted, 1);
                    #[allow(clippy::cast_precision_loss)]
                    let uniqueness_ratio = (cardinality as f64) / (record_count as f64);
                    mc_pieces.extend_from_slice(&[
                        itoa::Buffer::new().format(cardinality).to_owned(),
                        util::round_num(uniqueness_ratio, round_places),
                    ]);
                }
                if self.which.mode {
                    // mode/s & antimode/s
                    if cardinality == record_count {
                        // all values unique
                        mc_pieces.extend_from_slice(
                            // modes - short-circuit modes calculation as there is none
                            &[
                                empty(),
                                "0".to_string(),
                                "0".to_string(),
                                // antimodes - instead of returning everything, just say *ALL
                                "*ALL".to_string(),
                                "0".to_string(),
                                "1".to_string(),
                            ],
                        );
                    } else {
                        let (
                            (modes_result, modes_count, mode_occurrences),
                            (antimodes_result, antimodes_count, antimode_occurrences),
                        ) = v.modes_antimodes();
                        // mode/s ============
                        let modes_list = if visualize_ws {
                            modes_result
                                .iter()
                                .map(|c| util::visualize_whitespace(&String::from_utf8_lossy(c)))
                                .join(&stats_separator)
                        } else {
                            modes_result
                                .iter()
                                .map(|c| String::from_utf8_lossy(c))
                                .join(&stats_separator)
                        };

                        // antimode/s ============
                        let antimodes_len = ANTIMODES_LEN.get_or_init(|| {
                            std::env::var("QSV_ANTIMODES_LEN")
                                .map(|val| {
                                    let parsed =
                                        val.parse::<usize>().unwrap_or(DEFAULT_ANTIMODES_LEN);
                                    // if 0, disable length limiting
                                    if parsed == 0 { usize::MAX } else { parsed }
                                })
                                .unwrap_or(DEFAULT_ANTIMODES_LEN)
                        });

                        let mut antimodes_list = String::with_capacity(*antimodes_len);

                        // We only store the first 10 antimodes
                        // so if antimodes_count > 10, add the "*PREVIEW: " prefix
                        if antimodes_count > MAX_ANTIMODES {
                            antimodes_list.push_str("*PREVIEW: ");
                        }

                        let antimodes_vals = &antimodes_result
                            .iter()
                            .map(|c| String::from_utf8_lossy(c))
                            .join(&stats_separator);

                        // if the antimodes result starts with the separator,
                        // it indicates that NULL is the first antimode. Add NULL to the list.
                        if antimodes_vals.starts_with(&stats_separator) {
                            antimodes_list.push_str("NULL");
                        }
                        antimodes_list.push_str(antimodes_vals);

                        // and truncate at antimodes_len characters with an ellipsis
                        if antimodes_list.len() > *antimodes_len {
                            util::utf8_truncate(&mut antimodes_list, *antimodes_len + 1);
                            antimodes_list.push_str("...");
                        }

                        mc_pieces.extend_from_slice(&[
                            // mode/s
                            modes_list,
                            modes_count.to_string(),
                            mode_occurrences.to_string(),
                            // antimode/s
                            if visualize_ws {
                                util::visualize_whitespace(&antimodes_list)
                            } else {
                                antimodes_list
                            },
                            antimodes_count.to_string(),
                            antimode_occurrences.to_string(),
                        ]);
                    }
                }
            },
        }

        // type
        if cardinality == 2 && infer_boolean {
            // if cardinality is 2, it's a boolean if its in the true/false patterns
            let patterns = BOOLEAN_PATTERNS.get();
            if let Some(patterns) = patterns {
                let mut is_boolean = false;
                for pattern in patterns {
                    if pattern.matches(&minval).is_some() && pattern.matches(&maxval).is_some() {
                        pieces.push("Boolean".to_string());
                        is_boolean = true;
                        break;
                    }
                }
                if !is_boolean {
                    pieces.push(typ.to_string());
                }
            } else {
                pieces.push(typ.to_string());
            }
        } else {
            pieces.push(typ.to_string());
        }

        // we're doing --typesonly with --infer-boolean, we don't need to calculate anything else
        if self.which.typesonly && infer_boolean {
            return csv::StringRecord::from(pieces);
        }

        // is_ascii
        if typ == FieldType::TString {
            pieces.push(self.is_ascii.to_string());
        } else {
            pieces.push(empty());
        }

        // sum
        let stotlen =
            if let Some((stotlen_work, sum)) = self.sum.as_ref().and_then(|sum| sum.show(typ)) {
                if typ == FieldType::TFloat {
                    if let Ok(f64_val) = fast_float2::parse::<f64, &[u8]>(sum.as_bytes()) {
                        pieces.push(util::round_num(f64_val, round_places));
                    } else {
                        pieces.push(format!("ERROR: Cannot convert {sum} to a float."));
                    }
                } else {
                    pieces.push(sum);
                }
                stotlen_work
            } else {
                pieces.push(empty());
                0
            };

        // min/max/range/sort_order
        // actually append it here - to preserve legacy ordering of columns
        pieces.extend_from_slice(&minmax_range_sortorder_pieces);

        // min/max/sum/avg/stddev/variance/cv length
        // we only show string length stats for String type
        if typ != FieldType::TString {
            pieces.extend_from_slice(&[
                empty(),
                empty(),
                empty(),
                empty(),
                empty(),
                empty(),
                empty(),
            ]);
        } else if let Some(mm) = self.minmax.as_ref().and_then(TypedMinMax::len_range) {
            // we have a min/max length
            pieces.extend_from_slice(&[mm.0, mm.1]);
            if stotlen < u64::MAX {
                pieces.push(itoa::Buffer::new().format(stotlen).to_owned());
                #[allow(clippy::cast_precision_loss)]
                let avg_len = stotlen as f64 / record_count as f64;
                pieces.push(util::round_num(avg_len, round_places));

                if let Some(vl) = self.online_len.as_ref() {
                    let vlen_stddev = vl.stddev();
                    let vlen_variance = vl.variance();
                    pieces.push(util::round_num(vlen_stddev, round_places));
                    pieces.push(util::round_num(vlen_variance, round_places));
                    pieces.push(util::round_num(vlen_stddev / avg_len, round_places));
                } else {
                    pieces.push(empty());
                    pieces.push(empty());
                    pieces.push(empty());
                }
            } else {
                // we saturated the sum of string lengths, it means we had an overflow
                // so we return OVERFLOW_STRING for sum,avg,stddev,variance length
                pieces.extend_from_slice(&[
                    OVERFLOW_STRING.to_string(),
                    OVERFLOW_STRING.to_string(),
                    OVERFLOW_STRING.to_string(),
                    OVERFLOW_STRING.to_string(),
                    OVERFLOW_STRING.to_string(),
                ]);
            }
        } else {
            pieces.extend_from_slice(&[
                empty(),
                empty(),
                empty(),
                empty(),
                empty(),
                empty(),
                empty(),
            ]);
        }

        // mean, sem, geometric_mean, harmonic_mean, stddev, variance & cv
        if typ == TString || typ == TNull {
            pieces.extend_from_slice(&[
                empty(),
                empty(),
                empty(),
                empty(),
                empty(),
                empty(),
                empty(),
            ]);
        } else if let Some(ref v) = self.online {
            let std_dev = v.stddev();
            #[allow(clippy::cast_precision_loss)]
            let sem = std_dev / (v.len() as f64).sqrt();
            let mean = v.mean();
            let cv = (std_dev / mean) * 100_f64;
            let geometric_mean = v.geometric_mean();
            let harmonic_mean = v.harmonic_mean();
            if self.typ == TFloat || self.typ == TInteger {
                pieces.extend_from_slice(&[
                    util::round_num(mean, round_places),
                    util::round_num(sem, round_places),
                    util::round_num(geometric_mean, round_places),
                    util::round_num(harmonic_mean, round_places),
                    util::round_num(std_dev, round_places),
                    util::round_num(v.variance(), round_places),
                    util::round_num(cv, round_places),
                ]);
            } else {
                // by the time we get here, the type is a TDateTime or TDate
                pieces.push(timestamp_ms_to_rfc3339(mean as i64, typ));
                // instead of returning sem, stdev & variance as timestamps, return it in
                // days as its more human readable and practical for real-world use cases
                // Round to at least 5 decimal places, so we have millisecond precision
                pieces.push(util::round_num(
                    sem / MS_IN_DAY,
                    u32::max(round_places, DAY_DECIMAL_PLACES),
                ));
                pieces.push(util::round_num(
                    geometric_mean / MS_IN_DAY,
                    u32::max(round_places, DAY_DECIMAL_PLACES),
                ));
                pieces.push(util::round_num(
                    harmonic_mean / MS_IN_DAY,
                    u32::max(round_places, DAY_DECIMAL_PLACES),
                ));
                pieces.push(util::round_num(
                    std_dev / MS_IN_DAY,
                    u32::max(round_places, DAY_DECIMAL_PLACES),
                ));
                pieces.push(util::round_num(
                    v.variance() / (MS_IN_DAY * MS_IN_DAY),
                    u32::max(round_places, DAY_DECIMAL_PLACES),
                ));
                pieces.push(util::round_num(cv, round_places));
            }
        } else {
            pieces.extend_from_slice(&[
                empty(),
                empty(),
                empty(),
                empty(),
                empty(),
                empty(),
                empty(),
            ]);
        }

        // nullcount
        pieces.push(itoa::Buffer::new().format(self.nullcount).to_owned());

        // max precision
        if typ == TFloat {
            pieces.push(self.max_precision.to_string());
        } else {
            pieces.push(empty());
        }

        // sparsity
        #[allow(clippy::cast_precision_loss)]
        let sparsity: f64 = self.nullcount as f64 / *RECORD_COUNT.get().unwrap_or(&1) as f64;
        pieces.push(util::round_num(sparsity, round_places));

        // quartiles
        // as q2==median, cache and reuse it if the --median or --mad flags are set
        let mut existing_median = None;
        let mut quartile_pieces = Vec::with_capacity(9);
        match self.unsorted_stats.as_mut().and_then(|v| match typ {
            TInteger | TFloat | TDate | TDateTime => {
                if self.which.quartiles {
                    v.quartiles()
                } else {
                    None
                }
            },
            _ => None,
        }) {
            None => {
                if self.which.quartiles {
                    quartile_pieces.extend_from_slice(&[
                        empty(),
                        empty(),
                        empty(),
                        empty(),
                        empty(),
                        empty(),
                        empty(),
                        empty(),
                        empty(),
                    ]);
                }
            },
            Some((q1, q2, q3)) => {
                existing_median = Some(q2);
                let iqr = q3 - q1;

                // use fused multiply add (mul_add) when possible
                // fused mul_add is more accurate & is more performant if the
                // target architecture has a dedicated `fma` CPU instruction
                // https://doc.rust-lang.org/std/primitive.f64.html#method.mul_add

                // lower_outer_fence = "q1 - (3.0 * iqr)"
                let lof = 3.0f64.mul_add(-iqr, q1);
                // lower_inner_fence = "q1 - (1.5 * iqr)"
                let lif = 1.5f64.mul_add(-iqr, q1);

                // upper inner fence = "q3 + (1.5 * iqr)"
                let uif = 1.5_f64.mul_add(iqr, q3);
                // upper_outer_fence = "q3 + (3.0 * iqr)"
                let uof = 3.0_f64.mul_add(iqr, q3);

                // calculate skewness using Quantile-based measures
                // https://en.wikipedia.org/wiki/Skewness#Quantile-based_measures
                // https://blogs.sas.com/content/iml/2017/07/19/quantile-skewness.html
                // quantile skewness = ((q3 - q2) - (q2 - q1)) / iqr;
                // which is also (q3 - (2.0 * q2) + q1) / iqr
                // which in turn, is the basis of the fused multiply add version below
                let skewness = (2.0f64.mul_add(-q2, q3) + q1) / iqr;

                if typ == TDateTime || typ == TDate {
                    // casting from f64 to i64 is OK, per
                    // https://doc.rust-lang.org/reference/expressions/operator-expr.html#numeric-cast
                    // as values larger/smaller than what i64 can handle will automatically
                    // saturate to i64 max/min values.
                    quartile_pieces.extend_from_slice(&[
                        timestamp_ms_to_rfc3339(lof as i64, typ),
                        timestamp_ms_to_rfc3339(lif as i64, typ),
                        timestamp_ms_to_rfc3339(q1 as i64, typ),
                        timestamp_ms_to_rfc3339(q2 as i64, typ), // q2 = median
                        timestamp_ms_to_rfc3339(q3 as i64, typ),
                        // return iqr in days - there are 86,400,000 ms in a day
                        util::round_num(
                            (q3 - q1) / MS_IN_DAY,
                            u32::max(round_places, DAY_DECIMAL_PLACES),
                        ),
                        timestamp_ms_to_rfc3339(uif as i64, typ),
                        timestamp_ms_to_rfc3339(uof as i64, typ),
                    ]);
                } else {
                    quartile_pieces.extend_from_slice(&[
                        util::round_num(lof, round_places),
                        util::round_num(lif, round_places),
                        util::round_num(q1, round_places),
                        util::round_num(q2, round_places), // q2 = median
                        util::round_num(q3, round_places),
                        util::round_num(iqr, round_places),
                        util::round_num(uif, round_places),
                        util::round_num(uof, round_places),
                    ]);
                }
                quartile_pieces.push(util::round_num(skewness, round_places));
            },
        }

        // median
        if let Some(v) = self.unsorted_stats.as_mut().and_then(|v| {
            if let TNull | TString = typ {
                None
            } else if let Some(existing_median) = existing_median {
                // if we already calculated the q2 (median) in the quartiles, return it
                if self.which.median {
                    Some(existing_median)
                } else {
                    None
                }
            } else if self.which.median {
                // otherwise, calculate the median
                v.median()
            } else {
                None
            }
        }) {
            if typ == TDateTime || typ == TDate {
                pieces.push(timestamp_ms_to_rfc3339(v as i64, typ));
            } else {
                pieces.push(util::round_num(v, round_places));
            }
        } else if self.which.median {
            pieces.push(empty());
        }

        // median absolute deviation (MAD)
        if let Some(v) = self.unsorted_stats.as_mut().and_then(|v| {
            if let TNull | TString = typ {
                None
            } else if self.which.mad {
                v.mad(existing_median)
            } else {
                None
            }
        }) {
            if typ == TDateTime || typ == TDate {
                // like stddev, return MAD in days when the type is a date or datetime
                pieces.push(util::round_num(
                    v / MS_IN_DAY,
                    u32::max(round_places, DAY_DECIMAL_PLACES),
                ));
            } else {
                pieces.push(util::round_num(v, round_places));
            }
        } else if self.which.mad {
            pieces.push(empty());
        }

        // quartiles
        // append it here to preserve legacy ordering of columns
        pieces.extend_from_slice(&quartile_pieces);

        // mode/modes/antimodes & cardinality
        // append it here to preserve legacy ordering of columns
        pieces.extend_from_slice(&mc_pieces);

        // Add percentiles after quartiles
        if let Some(v) = self.unsorted_stats.as_mut() {
            match typ {
                TInteger | TFloat | TDate | TDateTime => {
                    let percentile_list = self
                        .which
                        .percentile_list
                        .split(',')
                        .filter_map(|p| p.trim().parse::<f64>().ok())
                        .map(|p| p as u8)
                        .collect::<Vec<_>>();

                    if let Some(percentile_values) = v.custom_percentiles(&percentile_list) {
                        let formatted_values = if typ == TDateTime || typ == TDate {
                            percentile_values
                                .iter()
                                .map(|p| {
                                    // Explicitly cast f64 to i64 for timestamp conversion
                                    #[allow(clippy::cast_possible_truncation)]
                                    let ts = p.round() as i64;
                                    timestamp_ms_to_rfc3339(ts, typ)
                                })
                                .collect::<Vec<_>>()
                        } else {
                            percentile_values
                                .iter()
                                .map(|p| util::round_num(*p, round_places))
                                .collect::<Vec<_>>()
                        };
                        pieces.push(formatted_values.join(&stats_separator));
                    } else {
                        pieces.push(empty());
                    }
                },
                _ => pieces.push(empty()),
            }
        } else if self.which.percentiles {
            pieces.push(empty());
        }

        if dataset_stats {
            // add an empty field for qsv__value
            pieces.push(empty());
        }

        csv::StringRecord::from(pieces)
    }
}

impl Commute for Stats {
    #[inline]
    fn merge(&mut self, other: Stats) {
        self.typ.merge(other.typ);
        self.is_ascii &= other.is_ascii;
        self.max_precision = self.max_precision.max(other.max_precision);
        self.which.merge(other.which);
        self.nullcount += other.nullcount;
        self.sum_stotlen = self.sum_stotlen.saturating_add(other.sum_stotlen);
        self.sum.merge(other.sum);
        self.modes.merge(other.modes);
        self.unsorted_stats.merge(other.unsorted_stats);
        self.online.merge(other.online);
        self.online_len.merge(other.online_len);
        self.minmax.merge(other.minmax);
    }
}

#[allow(clippy::enum_variant_names)]
#[allow(clippy::unsafe_derive_deserialize)]
#[derive(Clone, Copy, PartialEq, Default, Serialize, Deserialize)]
enum FieldType {
    // The default - TNull, is the most specific type.
    // Type inference proceeds by assuming the most specific type and then
    // relaxing the type as counter-examples are found.
    #[default]
    TNull,
    TString,
    TFloat,
    TInteger,
    TDate,
    TDateTime,
}

impl FieldType {
    /// infer data type from a given sample & current type inference
    /// infer_dates signals if date inference should be attempted
    /// returns the inferred type and if infer_dates is true,
    /// the date in ms since the epoch if the type is a date or datetime
    /// otherwise, 0
    #[allow(clippy::inline_always)]
    #[inline(always)]
    pub fn from_sample(
        infer_dates: bool,
        prefer_dmy: bool,
        sample: &[u8],
        current_type: FieldType,
    ) -> (FieldType, i64) {
        // faster than sample.len() == 0 or sample.is_empty() per microbenchmarks
        if b"" == sample {
            return (FieldType::TNull, 0);
        }

        // no need to do type checking if current_type is already a String
        if current_type == FieldType::TString {
            return (FieldType::TString, 0);
        }

        if let Ok(samp_int) = atoi_simd::parse::<i64>(sample) {
            // Check for integer, with leading zero check for strings like zip codes
            // safety: we know sample is not null as we checked earlier
            if samp_int == 0 || unsafe { *sample.get_unchecked(0) != b'0' } {
                return (FieldType::TInteger, 0);
            }
            // If starts with '0' and a valid integer != 0, it's a string with a leading zero
            return (FieldType::TString, 0);
        }

        // Check for float
        // we use fast_float2 as it doesn't need to validate the sample as UTF-8 first
        if fast_float2::parse::<f64, &[u8]>(sample).is_ok() {
            return (FieldType::TFloat, 0);
        }

        // Only attempt UTF-8 validation and date parsing if infer_dates is true
        if !infer_dates {
            return (FieldType::TString, 0);
        }

        // Check if valid UTF-8 first, return early if not
        if let Ok(s) = simdutf8::basic::from_utf8(sample) {
            // Try date parsing
            if let Ok(parsed_date) = parse_with_preference(s, prefer_dmy) {
                let ts_val = parsed_date.timestamp_millis();
                return if ts_val % MS_IN_DAY_INT == 0 {
                    // if the date is a whole number of days, return as a date
                    (FieldType::TDate, ts_val)
                } else {
                    // otherwise, return as a datetime
                    (FieldType::TDateTime, ts_val)
                };
            }
        } else {
            // If not valid UTF-8, it's a binary string, return as TString
            return (FieldType::TString, 0);
        }

        // Default to TString if none of the above conditions are met
        (FieldType::TString, 0)
    }
}

impl Commute for FieldType {
    #[inline]
    #[allow(clippy::match_same_arms)]
    // we allow match_same_arms because we want are optimizing for
    // performance and not readability, as match arms are evaluated in order
    // so we want to put the most common cases first
    fn merge(&mut self, other: FieldType) {
        *self = match (*self, other) {
            (TString, TString) => TString,
            (TFloat, TFloat) => TFloat,
            (TInteger, TInteger) => TInteger,
            // Null does not impact the type.
            (TNull, any) | (any, TNull) => any,
            // Integers can degrade to floats.
            (TFloat, TInteger) | (TInteger, TFloat) => TFloat,
            // date data types
            (TDate, TDate) => TDate,
            (TDateTime | TDate, TDateTime) | (TDateTime, TDate) => TDateTime,
            // anything else is a String
            (_, _) => TString,
        };
    }
}

impl fmt::Display for FieldType {
    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
        match *self {
            TNull => write!(f, "NULL"),
            TString => write!(f, "String"),
            TFloat => write!(f, "Float"),
            TInteger => write!(f, "Integer"),
            TDate => write!(f, "Date"),
            TDateTime => write!(f, "DateTime"),
        }
    }
}

impl fmt::Debug for FieldType {
    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
        match *self {
            TNull => write!(f, "NULL"),
            TString => write!(f, "String"),
            TFloat => write!(f, "Float"),
            TInteger => write!(f, "Integer"),
            TDate => write!(f, "Date"),
            TDateTime => write!(f, "DateTime"),
        }
    }
}

/// `TypedSum` keeps a rolling sum of the data seen.
/// It sums integers until it sees a float, at which point it sums floats.
/// It also counts the total length of strings.
#[derive(Clone, Default, Serialize, Deserialize, PartialEq)]
struct TypedSum {
    float:   Option<f64>,
    integer: i64,
    stotlen: u64, // sum of the total length of strings
}

impl TypedSum {
    #[allow(clippy::inline_always)]
    #[inline(always)]
    fn add(&mut self, typ: FieldType, sample: &[u8]) {
        if b"" == sample {
            return;
        }
        #[allow(clippy::cast_precision_loss)]
        match typ {
            TFloat => {
                if let Ok(float_sample) = fast_float2::parse::<f64, &[u8]>(sample) {
                    if let Some(ref mut f) = self.float {
                        *f += float_sample;
                    } else {
                        self.float = Some((self.integer as f64) + float_sample);
                    }
                }
            },
            TInteger => {
                if let Some(ref mut float) = self.float {
                    // safety: we know that the sample is a valid f64
                    *float += fast_float2::parse::<f64, &[u8]>(sample).unwrap();
                } else {
                    // so we don't panic on overflow/underflow, use saturating_add
                    self.integer = self
                        .integer
                        // safety: we know that the sample is a valid i64
                        .saturating_add(atoi_simd::parse::<i64>(sample).unwrap());
                }
            },
            TString => {
                self.stotlen = self.stotlen.saturating_add(sample.len() as u64);
            },
            // we don't need to do anything for TNull, TDate or TDateTime
            // as they don't have a sum
            _ => {},
        }
    }

    fn show(&self, typ: FieldType) -> Option<(u64, String)> {
        match typ {
            TNull | TDate | TDateTime => None,
            TInteger => {
                match self.integer {
                    // with saturating_add, if this is equal to i64::MAX or i64::MIN
                    // we overflowed/underflowed
                    i64::MAX => Some((self.stotlen, OVERFLOW_STRING.to_string())),
                    i64::MIN => Some((self.stotlen, UNDERFLOW_STRING.to_string())),
                    _ => Some((
                        self.stotlen,
                        itoa::Buffer::new().format(self.integer).to_owned(),
                    )),
                }
            },
            TFloat => Some((
                self.stotlen,
                ryu::Buffer::new()
                    .format(self.float.unwrap_or(0.0))
                    .to_owned(),
            )),
            TString => Some((self.stotlen, String::new())),
        }
    }
}

impl Commute for TypedSum {
    #[inline]
    fn merge(&mut self, other: TypedSum) {
        #[allow(clippy::cast_precision_loss)]
        match (self.float, other.float) {
            (Some(f1), Some(f2)) => self.float = Some(f1 + f2),
            (Some(f1), None) => self.float = Some(f1 + (other.integer as f64)),
            (None, Some(f2)) => self.float = Some((self.integer as f64) + f2),
            (None, None) => self.integer = self.integer.saturating_add(other.integer),
        }
        self.stotlen = self.stotlen.saturating_add(other.stotlen);
    }
}

/// `TypedMinMax` keeps track of minimum/maximum/range/sort_order values for each possible type
/// where min/max/range/sort_order makes sense.
#[derive(Clone, Default, Serialize, Deserialize, PartialEq)]
struct TypedMinMax {
    floats:   MinMax<f64>,
    integers: MinMax<i64>,
    dates:    MinMax<i64>,
    strings:  MinMax<Vec<u8>>,
    str_len:  MinMax<usize>,
}

impl TypedMinMax {
    #[inline]
    fn add(&mut self, typ: FieldType, sample: &[u8]) {
        let sample_len = sample.len();
        if sample_len == 0 {
            self.str_len.add(0);
            return;
        }
        // safety: we can use unwrap below since we know the data type of the sample
        match typ {
            TString => {
                self.str_len.add(sample_len);
                self.strings.add(sample.to_vec());
            },
            TFloat => {
                let n = fast_float2::parse::<f64, &[u8]>(sample).unwrap();

                self.floats.add(n);
                self.integers.add(n as i64);
            },
            TInteger => {
                let n = atoi_simd::parse::<i64>(sample).unwrap();
                self.integers.add(n);
                #[allow(clippy::cast_precision_loss)]
                self.floats.add(n as f64);
            },
            TNull => {},
            // it must be a TDate or TDateTime
            // we use "_" here instead of "TDate | TDateTime" for the match to avoid
            // the overhead of matching on the OR value, however minor
            _ => {
                let n = atoi_simd::parse::<i64>(sample).unwrap();
                self.dates.add(n);
            },
        }
    }

    fn len_range(&self) -> Option<(String, String)> {
        if let (Some(min), Some(max)) = (self.str_len.min(), self.str_len.max()) {
            Some((
                itoa::Buffer::new().format(*min).to_owned(),
                itoa::Buffer::new().format(*max).to_owned(),
            ))
        } else {
            None
        }
    }

    #[inline]
    fn show(
        &self,
        typ: FieldType,
        round_places: u32,
        visualize_ws: bool,
    ) -> Option<(String, String, String, String, String)> {
        match typ {
            TNull => None,
            TString => {
                if let (Some(min), Some(max), sort_order, sortiness) = (
                    self.strings.min(),
                    self.strings.max(),
                    self.strings.sort_order(),
                    self.strings.sortiness(),
                ) {
                    let min_str = String::from_utf8_lossy(min).to_string();
                    let max_str = String::from_utf8_lossy(max).to_string();
                    let (min_display, max_display) = if visualize_ws {
                        (
                            util::visualize_whitespace(&min_str),
                            util::visualize_whitespace(&max_str),
                        )
                    } else {
                        (min_str, max_str)
                    };
                    Some((
                        min_display,
                        max_display,
                        String::new(),
                        sort_order.to_string(),
                        util::round_num(sortiness, round_places),
                    ))
                } else {
                    None
                }
            },
            TInteger => {
                if let (Some(min), Some(max), sort_order, sortiness) = (
                    self.integers.min(),
                    self.integers.max(),
                    self.integers.sort_order(),
                    self.integers.sortiness(),
                ) {
                    Some((
                        itoa::Buffer::new().format(*min).to_owned(),
                        itoa::Buffer::new().format(*max).to_owned(),
                        itoa::Buffer::new().format(*max - *min).to_owned(),
                        sort_order.to_string(),
                        util::round_num(sortiness, round_places),
                    ))
                } else {
                    None
                }
            },
            TFloat => {
                if let (Some(min), Some(max), sort_order, sortiness) = (
                    self.floats.min(),
                    self.floats.max(),
                    self.floats.sort_order(),
                    self.floats.sortiness(),
                ) {
                    Some((
                        ryu::Buffer::new().format(*min).to_owned(),
                        ryu::Buffer::new().format(*max).to_owned(),
                        util::round_num(*max - *min, round_places),
                        sort_order.to_string(),
                        util::round_num(sortiness, round_places),
                    ))
                } else {
                    None
                }
            },
            TDateTime | TDate => {
                if let (Some(min), Some(max), sort_order, sortiness) = (
                    self.dates.min(),
                    self.dates.max(),
                    self.dates.sort_order(),
                    self.dates.sortiness(),
                ) {
                    Some((
                        timestamp_ms_to_rfc3339(*min, typ),
                        timestamp_ms_to_rfc3339(*max, typ),
                        // return in days, not timestamp in milliseconds
                        #[allow(clippy::cast_precision_loss)]
                        util::round_num(
                            (*max - *min) as f64 / MS_IN_DAY,
                            u32::max(round_places, 5),
                        ),
                        sort_order.to_string(),
                        util::round_num(sortiness, round_places),
                    ))
                } else {
                    None
                }
            },
        }
    }
}

impl Commute for TypedMinMax {
    #[inline]
    fn merge(&mut self, other: TypedMinMax) {
        self.floats.merge(other.floats);
        self.integers.merge(other.integers);
        self.dates.merge(other.dates);
        self.strings.merge(other.strings);
        self.str_len.merge(other.str_len);
    }
}