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0.3.5 ... main

Author SHA1 Message Date
Guillem Borrell c92d1ad1df Merge branch 'main' of https://git.guillemborrell.es/guillem/dr
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2023-06-29 09:45:20 +00:00
Guillem Borrell 9033f81b98 Upgraded to Polars 0.27 2023-02-11 17:55:29 +00:00
Guillem Borrell Nogueras f4b3a525bb Fixed bash string quotes
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2023-02-01 10:37:28 +01:00
Guillem Borrell Nogueras c678cc27f3 Added an examples section.
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2023-02-01 10:36:24 +01:00
Guillem Borrell 8180def799 Sometimes you can be lazy, sometimes you can't
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2023-01-23 14:06:39 +00:00
Guillem Borrell cabc0e7dfe HEADER option ignores the header.
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2023-01-23 14:03:52 +00:00
Guillem Borrell 02c6b50d00 Added more documentation in the cli
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2023-01-20 10:46:14 +00:00
Guillem Borrell 83a4138f64 Some relevant docstrings
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2023-01-19 12:28:50 +00:00
Guillem Borrell 92fec23932 Version bump
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2023-01-19 12:21:16 +00:00
Guillem Borrell f0730efcd9 Refactoring of the main
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2023-01-19 12:20:53 +00:00
Guillem Borrell cbf318690c Merge branch 'main' of https://git.guillemborrell.es/guillem/dr
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2023-01-18 09:50:30 +00:00
Guillem Borrell edaea203b7 Infer with no limits 2023-01-18 09:50:02 +00:00
Guillem Borrell 951bd82a2b Improved documentation
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2023-01-17 18:59:16 +00:00
Guillem Borrell 4e94ad295b Fixed bug
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2023-01-17 18:12:09 +00:00
Guillem Borrell d534bdef8d Schemas can be generated reading csv from stdin
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2023-01-17 18:07:44 +00:00
Guillem Borrell 99d58ff9c3 Don't sink parquet for he moment
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2023-01-17 17:54:48 +00:00
Guillem Borrell facae6af40 Support other delimiters
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2023-01-17 14:48:05 +00:00
Guillem Borrell 1858777c69 Handle empty columns in schema summary too
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2023-01-17 12:36:09 +00:00
Guillem Borrell 59adb12078 Handle empty column names
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2023-01-17 12:34:10 +00:00
Guillem Borrell 1e18c9ae9f Read csv from stdin too
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2023-01-17 12:18:08 +00:00
Guillem Borrell e4e9b71674 Read csv from stdin too
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2023-01-17 11:50:32 +00:00
Guillem Borrell e29b3d18e8 Merge branch 'main' of https://git.guillemborrell.es/guillem/dr
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2023-01-17 11:29:30 +00:00
Guillem Borrell 717da2e1b6 First attempt at db insertion support 2023-01-17 11:28:49 +00:00
Guillem Borrell Nogueras 4c26c4c344 revert 98a2a983a9
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revert Build binaries in github.com
2022-12-26 22:45:35 +01:00
Guillem Borrell Nogueras 98a2a983a9 Build binaries in github.com
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2022-12-26 20:33:50 +01:00
Guillem Borrell Nogueras 06a197b07c Testing github actions
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2022-12-26 16:11:45 +01:00
Guillem Borrell fc063601c5 Bring back parquet from stdin. Fixes.
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2022-12-26 12:54:17 +00:00
Guillem Borrell 2b18f7b5e3 Multiple improvements
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2022-12-25 23:07:50 +00:00
Guillem Borrell 3af97c71f0 Fixed compile time issue
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2022-12-25 09:22:24 +00:00
Guillem Borrell ada122e5c3 Polars version bump. Compile time erro fix
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2022-12-25 09:02:13 +00:00
Guillem Borrell 7ecee28ddf Merge branch 'main' of https://git.guillemborrell.es/guillem/dr
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2022-12-20 08:10:45 +00:00
Guillem Borrell 78bbaa46e8 Cleanup project 2022-12-20 08:10:15 +00:00
Guillem Borrell Nogueras 681e13525d Added more links
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2022-12-20 08:24:46 +01:00
Guillem Borrell 2a9937135c Fix package version
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2022-12-06 15:08:54 +00:00
Guillem Borrell 79a8a82bea Implemented functionalities for 0.4
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2022-12-06 14:16:39 +00:00
Guillem Borrell 978afec7f4 exe for windows
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2022-12-02 18:44:19 +00:00
Guillem Borrell 7d54ee9874 Fix package upload
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2022-12-02 14:11:49 +00:00
Guillem Borrell 7f921fac2a configure linker
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2022-12-02 13:38:40 +00:00
Guillem Borrell acd38bffb8 Let's try another version of debian
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2022-12-01 16:17:36 +00:00
12 changed files with 643 additions and 561 deletions

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@ -10,7 +10,8 @@ pipeline:
when:
event: tag
commands:
- apt-get update && apt-get install -y gcc-12-aarch64-linux-gnu
- export CARGO_TARGET_AARCH64_UNKNOWN_LINUX_GNU_LINKER=aarch64-linux-gnu-gcc
- apt-get update && apt-get install -y gcc-aarch64-linux-gnu
- rustup target add aarch64-unknown-linux-gnu
- cargo build --release --target aarch64-unknown-linux-gnu
buildwinamd64:
@ -18,7 +19,7 @@ pipeline:
when:
event: tag
commands:
- apt-get update && apt-get install -y gcc-mingw-w64-base
- apt-get update && apt-get install -y gcc-mingw-w64
- rustup target add x86_64-pc-windows-gnu
- cargo build --release --target x86_64-pc-windows-gnu
release:
@ -27,9 +28,9 @@ pipeline:
event: tag
secrets: [ gitea_api_key ]
commands:
- curl --user guillem:$GITEA_API_KEY --upload-file target/release/dr https://git.guillemborrell.es/api/packages/guillem/generic/dr/$CI_COMMIT_TAG/dr-linux-amd64
- curl --user guillem:$GITEA_API_KEY --upload-file target/aarch64-unknown-linux-gnu/release/dr https://git.guillemborrell.es/api/packages/guillem/generic/dr/$CI_COMMIT_TAG/dr-linux-aarch64
- curl --user guillem:$GITEA_API_KEY --upload-file target/x86_64-pc-windows-gnu/release/dr https://git.guillemborrell.es/api/packages/guillem/generic/dr/$CI_COMMIT_TAG/dr-win-amd64
- curl --user guillem:$GITEA_API_KEY --upload-file target/release/dr https://git.guillemborrell.es/api/packages/guillem/generic/dr/$CI_COMMIT_TAG-linux-amd64/dr
- curl --user guillem:$GITEA_API_KEY --upload-file target/aarch64-unknown-linux-gnu/release/dr https://git.guillemborrell.es/api/packages/guillem/generic/dr/$CI_COMMIT_TAG-linux-aarch64/dr
- curl --user guillem:$GITEA_API_KEY --upload-file target/x86_64-pc-windows-gnu/release/dr.exe https://git.guillemborrell.es/api/packages/guillem/generic/dr/$CI_COMMIT_TAG-win-amd64/dr.exe
publish:
image: rust:1-bullseye
when:

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@ -1,7 +1,7 @@
[package]
name = "dr"
description = "Command-line data file processing in Rust"
version = "0.3.1"
version = "0.7.0"
edition = "2021"
include = [
"**/*.rs",
@ -14,7 +14,8 @@ repository = "https://git.guillemborrell.es/guillem/dr"
[dependencies]
clap = {version = "4.0", features = ["cargo"]}
polars = "0.25"
polars-sql = "0.2.1"
polars-lazy = "0.25"
polars-io = {"version" = "0.25", features = ["parquet"]}
polars-lazy = {"version" = "0.27", "features" = ["parquet", "ipc", "csv-file"]}
polars-core = {"version" = "0.27", "features" = ["describe", "fmt"]}
polars-io = {"version" = "0.27", "features" = ["ipc_streaming"]}
polars-sql = {"version" = "0.2.3"}
sea-query = {"version" = "0.28"}

304
README.md
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@ -1,71 +1,68 @@
# dr.rs
[![status-badge](https://ci.guillemborrell.es/api/badges/guillem/dr/status.svg)](https://ci.guillemborrell.es/guillem/dr) | [Download](https://git.guillemborrell.es/guillem/-/packages/generic/dr)
[![status-badge](https://ci.guillemborrell.es/api/badges/guillem/dr/status.svg)](https://ci.guillemborrell.es/guillem/dr) | [Download](https://git.guillemborrell.es/guillem/-/packages/generic/dr) | [Source](https://git.guillemborrell.es/guillem/dr) | [Bugs](https://github.com/guillemborrell/dr)
A toolkit to process data files (csv and parquet) using the command line, inspired by [csvkit](https://github.com/wireservice/csvkit), with blazing speed, and powered by Rust.
You may wonder why I'm implementing this, since there's already [xsv](https://github.com/BurntSushi/xsv). There are two reasons for that:
1. This what I'm implementing to learn Rust
1. This what I'm implementing to learn Rust.
2. The Rust data ecosystem has evolved immensely since xsv was sarted. Now we can add things like SQL commands to filter csv files, or translate results to parquet files.
## Example
```bash
$ head wine.csv
Wine,Alcohol,Malic.acid,Ash,Acl,Mg,Phenols,Flavanoids,Nonflavanoid.phenols,Proanth,Color.int,Hue,OD,Proline
1,14.23,1.71,2.43,15.6,127,2.8,3.06,.28,2.29,5.64,1.04,3.92,1065
1,13.2,1.78,2.14,11.2,100,2.65,2.76,.26,1.28,4.38,1.05,3.4,1050
1,13.16,2.36,2.67,18.6,101,2.8,3.24,.3,2.81,5.68,1.03,3.17,1185
1,14.37,1.95,2.5,16.8,113,3.85,3.49,.24,2.18,7.8,.86,3.45,1480
1,13.24,2.59,2.87,21,118,2.8,2.69,.39,1.82,4.32,1.04,2.93,735
1,14.2,1.76,2.45,15.2,112,3.27,3.39,.34,1.97,6.75,1.05,2.85,1450
1,14.39,1.87,2.45,14.6,96,2.5,2.52,.3,1.98,5.25,1.02,3.58,1290
1,14.06,2.15,2.61,17.6,121,2.6,2.51,.31,1.25,5.05,1.06,3.58,1295
1,14.83,1.64,2.17,14,97,2.8,2.98,.29,1.98,5.2,1.08,2.85,1045
## TL;DR
You can install dr the rust way with `cargo install dr` but downloading a binary from [here](https://git.guillemborrell.es/guillem/-/packages/generic/dr) may be all you need.
$ cat wine.csv | dr sql "select Wine, avg(Alcohol) from this group by Wine" | dr print
shape: (3, 2)
┌──────┬───────────┐
│ Wine ┆ Alcohol │
│ --- ┆ --- │
│ i64 ┆ f64 │
╞══════╪═══════════╡
│ 3 ┆ 13.15375 │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 1 ┆ 13.744746 │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 2 ┆ 12.278732 │
└──────┴───────────┘
```
## Howto
The `dr` command offers a set of subcommands, each one of them with a different functionality. You can get the available subcommands with:
```bash
$ dr --help
Command-line data file processing in Rust
dr is a handy command line tool to handle csv and parquet files.
It is designed to integrate nicely with other command line tools
like cat, sed, awk and database clients cli. You can find more
information an a short tutorial https://git.guillemborrell.es/guillem/dr
Usage: dr [COMMAND]
Commands:
sql Runs a sql statement on the file
print Pretty prints the table
rpq Read parquet file
wpq Write to a parquet file
help Print this message or the help of the given subcommand(s)
csv
Read csv, output arrow stream
schema
Several table schema related utilities
sql
Runs a sql statement on the file
print
Pretty prints the table
rpq
Read parquet file
wpq
Write to a paquet file
help
Print this message or the help of the given subcommand(s)
Options:
-h, --help Print help information
-V, --version Print version information
-h, --help
Print help information (use `-h` for a summary)
-V, --version
Print version information
```
Subcommands can be pipelined unless reading from a file, writing to a file, or pretty prints data. What goes through the pipeline is a plain-text comma separated values with a header. While this may not be the best choice in terms of performance, allows `dr` subcommands to be combined with the usual unix-style command-line tools like `cat`, `head`, `grep`, `awk` and `sed`:
## Howto
```bash
$ cat wine.csv | head -n 5 | dr print
shape: (4, 14)
`dr` is convenience command to explore, transform, and analyze csv and parquet files to save you from writing throwaway python scripts or create a custom container image for verys simple tasks. It's designed to make the life of a data engineer a little easier.
Assume you have a very large csv file, and you just want to translate it to parquet with some type inference and sane defaults. With `dr` this is as easy as:
```
$ dr csv wine.csv -P wine.pq
```
Parquet files are binary, and you may want to check that you've not written nonsense by printing the header on your terminal.
```
$ dr rpq wine.pq -a
shape: (5, 14)
┌──────┬─────────┬────────────┬──────┬─────┬───────────┬──────┬──────┬─────────┐
│ Wine ┆ Alcohol ┆ Malic.acid ┆ Ash ┆ ... ┆ Color.int ┆ Hue ┆ OD ┆ Proline │
│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │
@ -78,112 +75,159 @@ shape: (4, 14)
│ 1 ┆ 13.16 ┆ 2.36 ┆ 2.67 ┆ ... ┆ 5.68 ┆ 1.03 ┆ 3.17 ┆ 1185 │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┤
│ 1 ┆ 14.37 ┆ 1.95 ┆ 2.5 ┆ ... ┆ 7.8 ┆ 0.86 ┆ 3.45 ┆ 1480 │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┤
│ 1 ┆ 13.24 ┆ 2.59 ┆ 2.87 ┆ ... ┆ 4.32 ┆ 1.04 ┆ 2.93 ┆ 735 │
└──────┴─────────┴────────────┴──────┴─────┴───────────┴──────┴──────┴─────────┘
```
Note that when `dr` loads csv data also tries to guess the data type of each field.
Maybe the most interesing feature of `dr` is the ability to process csv and parquet files using SQL, while solutions like `xsv` and `csvkit` rely on a rich set of subcommands and options. If you already know SQL, there's no need to read any more documentation to select, filter, or group data. The only thing you need to remember is that the table will be called `this`. The following command outputs a csv of the wine with the highest concentration of alcohol in the popular wine dataset:
### Parquet
`dr` is also useful to translate your csv files to parquet with a single command:
```bash
$ cat wine.csv | dr wpq wine.pq
```
Or explore parquet files
```bash
$ dr rpq wine.pq | head -n 5 | dr print
shape: (4, 14)
dr csv wine.csv -q "select * from this where Alcohol = max(Alcohol)" | dr print
shape: (1, 14)
┌──────┬─────────┬────────────┬──────┬─────┬───────────┬──────┬──────┬─────────┐
│ Wine ┆ Alcohol ┆ Malic.acid ┆ Ash ┆ ... ┆ Color.int ┆ Hue ┆ OD ┆ Proline │
│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │
│ i64 ┆ f64 ┆ f64 ┆ f64 ┆ ┆ f64 ┆ f64 ┆ f64 ┆ i64 │
╞══════╪═════════╪════════════╪══════╪═════╪═══════════╪══════╪══════╪═════════╡
│ 1 ┆ 14.23 ┆ 1.71 ┆ 2.43 ┆ ... ┆ 5.64 ┆ 1.04 ┆ 3.92 ┆ 1065 │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┤
│ 1 ┆ 13.2 ┆ 1.78 ┆ 2.14 ┆ ... ┆ 4.38 ┆ 1.05 ┆ 3.4 ┆ 1050 │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┤
│ 1 ┆ 13.16 ┆ 2.36 ┆ 2.67 ┆ ... ┆ 5.68 ┆ 1.03 ┆ 3.17 ┆ 1185 │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┤
│ 1 ┆ 14.37 ┆ 1.95 ┆ 2.5 ┆ ... ┆ 7.8 ┆ 0.86 ┆ 3.45 ┆ 1480 │
│ 1 ┆ 14.83 ┆ 1.64 ┆ 2.17 ┆ ... ┆ 5.2 ┆ 1.08 ┆ 2.85 ┆ 1045 │
└──────┴─────────┴────────────┴──────┴─────┴───────────┴──────┴──────┴─────────┘
```
If you don't use any option that formats the output of the results, `dr` outputs Arrow's IPC format, meaning that multiple `dr` calls can be efficiently chained with very low overhead. The following script loads one month of NY taxi data and executes two sql queries on the data.
```
$ dr rpq data/yellow_tripdata_2014-01.parquet \
-q "select count(1) as cnt, passenger_count from this group by passenger_count" \
| dr sql "select * from this order by cnt desc" \
| dr print
┌─────────┬─────────────────┐
│ cnt ┆ passenger_count │
│ --- ┆ --- │
│ u32 ┆ i64 │
╞═════════╪═════════════════╡
│ 9727321 ┆ 1 │
├╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 1891588 ┆ 2 │
├╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 789070 ┆ 5 │
├╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 566248 ┆ 3 │
├╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ ... ┆ ... │
├╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 19 ┆ 208 │
├╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 16 ┆ 9 │
├╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 7 ┆ 7 │
├╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 5 ┆ 8 │
└─────────┴─────────────────┘
```
### Operate with SQL databases
How many times did you have to insert a csv file (sometimes larger than memory) to a database? Tens of times? Hundreds? You've probably used Pandas for that, since it can infer the table's datatypes. So a simple data operation becomes a python script with Pandas and a driver for PostgreSQL as dependencies.
Now dr can provide the table creation statement with a handful of columns:
```
$ head wine.csv | dr schema -i -p -n wine
CREATE TABLE IF NOT EXISTS "wine" ( );
ALTER TABLE "wine" ADD COLUMN "Wine" integer;
ALTER TABLE "wine" ADD COLUMN "Alcohol" real;
ALTER TABLE "wine" ADD COLUMN "Malic.acid" real;
ALTER TABLE "wine" ADD COLUMN "Ash" real;
ALTER TABLE "wine" ADD COLUMN "Acl" real;
ALTER TABLE "wine" ADD COLUMN "Mg" integer;
ALTER TABLE "wine" ADD COLUMN "Phenols" real;
ALTER TABLE "wine" ADD COLUMN "Flavanoids" real;
ALTER TABLE "wine" ADD COLUMN "Nonflavanoid.phenols" real;
ALTER TABLE "wine" ADD COLUMN "Proanth" real;
ALTER TABLE "wine" ADD COLUMN "Color.int" real;
ALTER TABLE "wine" ADD COLUMN "Hue" real;
ALTER TABLE "wine" ADD COLUMN "OD" real;
ALTER TABLE "wine" ADD COLUMN "Proline" integer;
```
More about this in the Examples section
Since most databases can ingest and spit CSV files, some simple operations can be enhanced with dr, like storing the results of a query in a parquet file
```
$ psql -c "copy (select * from wine) to stdout with (FORMAT 'csv', HEADER)" | dr csv -i -P wine.pq
```
## Reference
Some commands that generate raw output in ipc format.
* Read a csv or parquet file and print the header: `dr {csv, rpq} [file] -a`
* Read a csv or parquet file, execute a SQL statement, and output the results in stdout using Arrow's ipc format `dr {csv, rpq} [file] -q "statement"`
* Read a csv or parquet file and print a summary of each column: `dr {csv, rpq} [file] -s "[query]"`
* Read a csv or parquet file, execute a query, and output the results in stdout using the csv format `dr {csv, rpq} [file] -s "[query]" -t`
* Read a csv and write a parquet file with the same contents: `dr csv [file.csv] -P [file.pq]`
Some commands that convert raw input in ipc format
* Read from stdin in ipc and pretty print the table: `dr print`
* Read from stdin in csv and pretty print the table: `dr print -t`
* Read from stdin in ipc and write the data in parquet: `dr wpq [file.pq]`
Some commands that read csv data from stdin
* Read csv from stdin and print the schema as it would be inserted in a postgresql database: `dr schema -i -p -n tablename`
* Reas csv from stdin and save as parquet, inferring types: `dr csv -i -P filename.pq`
## Examples
### Inserting CSV into postgres
Assume that you were given a large (several GiB) with a weird (latin1) encoding, and you want to insert it into postgres. This dataset may be too large to store it in memory in one go, so you'd like to stream it into the database. You need to
* Read the csv file
* Infer the schema, and create a table
* Change the encoding of the file to the same as the database
You can use `dr` to turn this into a two-step process, and pipe the encoding conversion in one go. The first step would be to infer the schema of the resulting table and creating the table
```
$ head large_csv_file.csv | iconv -f latin1 -t utf-8 | dr schema -i -p -n tablename | pgsql -U username -h hostname database
```
The second step would be leveraging the `pgsql` command to write the contents of the file into the database
```
$ cat large_csv_file.csv | iconv -f latin1 -t UTF-8 | psql -U username -h hostname -c "\copy tablename from stdin with (FORMAT 'csv', HEADER)" database
```
The ingestion process is atomic, meaning that if `pgsql` fails to insert any record, no insertions will be made at all. If the insertion fails, probably because some column of type varchar can't fit the inferred type, you can change the type with:
```
$ psql -U username -h hostname -c 'alter table tablename alter column "LongDescription" type varchar(1024);' database
```
And try inserting again
## Performance
`dr` is implemented in Rust with the goal of achieving the highest possible performance. Take for instance a simple read, groupby, and aggregate operation with ~30MB of data:
This command runs two dr processes. The first one makes an aggregation on a compressed parquet file of 144MB of size, and the second one just orders the result:
```bash
$ time cat data/walmart_train.csv | dr sql "select Dept, avg("Weekly_Sales") from this group by Dept" | dr print
shape: (81, 2)
┌──────┬──────────────┐
│ Dept ┆ Weekly_Sales │
│ --- ┆ --- │
│ i64 ┆ f64 │
╞══════╪══════════════╡
│ 30 ┆ 4118.197208 │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 16 ┆ 14245.63827 │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 56 ┆ 3833.706211 │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 24 ┆ 6353.604562 │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ ... ┆ ... │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 31 ┆ 2339.440287 │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 59 ┆ 694.463564 │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 27 ┆ 1583.437727 │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 77 ┆ 328.9618 │
└──────┴──────────────┘
real 0m0.089s
user 0m0.116s
sys 0m0.036s
```
$ dr rpq data/yellow_tripdata_2014-01.parquet \
-q "select count(1) as cnt, passenger_count from this group by passenger_count" \
| dr sql "select * from this order by cnt desc" \
> /dev/null
```
Let's compare that with the followint Python script that leverages Pandas to read the data, and compute the aggregation:
On a very very old machine (Intel(R) Core(TM) i5-6500T CPU @ 2.50GHz), this takes around half a second, which is roughly the time needed to read and decompress the parquet file. Polar's csv and parquet readers have some decent performance, so you can count on `dr` to be one of the fastest in the block.
```python
#!/usr/bin/env python3
## Caveats
import sys
import pandas as pd
df = pd.read_csv(sys.stdin)
print(df.groupby("Dept", sort=False, as_index=False).Weekly_Sales.mean())
```
```bash
$ time cat data/walmart_train.csv | ./python/group.py
Dept Weekly_Sales
0 1 19213.485088
1 2 43607.020113
2 3 11793.698516
3 4 25974.630238
4 5 21365.583515
.. ... ...
76 99 415.487065
77 39 11.123750
78 50 2658.897010
79 43 1.193333
80 65 45441.706224
[81 rows x 2 columns]
real 0m0.717s
user 0m0.627s
sys 0m0.282s
```
Note that there's roughly a 6x speedup. This considering that this operation in particular is heavily optimized in Pandas and most of the run time is spent in parsing and reading from stdin.
1. `dr` uses Polars to build and transform dataframes in Rust, and the entire table may be loaded in memory. At the time when `dr` was created, streaming support didn't get along very well with SQL contexts.
2. `dr` uses Polars' SQLContext to execute the query which supports a small subset of the SQL language.
## Built standing on the shoulders of giants

View file

@ -1,295 +0,0 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 39,
"id": "aa0bc87a-7fca-432d-9ad0-27855dfbc597",
"metadata": {},
"outputs": [],
"source": [
":dep csv = \"1.1\"\n",
":dep serde = \"1.0\"\n",
":dep polars = \"0.25\""
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "ab0babdd-8482-4248-bf3b-33ab0aad9e07",
"metadata": {},
"outputs": [
{
"ename": "Error",
"evalue": "unresolved import `polars::preamble`",
"output_type": "error",
"traceback": [
"\u001b[31m[E0432] Error:\u001b[0m unresolved import `polars::preamble`"
]
}
],
"source": [
"use std::fs::File;\n",
"use csv;\n",
"use std::collections::HashMap\n",
"use polars::preamble::*"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "561048d0-6982-4a87-97b8-4c31179cddf4",
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]
},
{
"data": {
"text/plain": [
"()"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type Record = HashMap<String,String>;\n",
"let file = File::open(\"../wine.csv\")?;\n",
"let mut r = csv::Reader::from_reader(file);\n",
"for result in r.byte_records() {\n",
" let record = result?;\n",
" println!(\"{:?}\", record)\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "45341565-f0f7-4bb7-a8e4-3623a9eded55",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Reader { core: Reader { dfa: Dfa(N/A), dfa_state: DfaState(0), nfa_state: StartRecord, delimiter: 44, term: CRLF, quote: 34, escape: None, double_quote: true, comment: None, quoting: true, use_nfa: false, line: 1, has_read: false, output_pos: 0 }, rdr: BufReader { reader: File { fd: 4, path: \"/home/jovyan/csvgr/wine.csv\", read: true, write: false }, buffer: 0/8192 }, state: ReaderState { headers: None, has_headers: true, flexible: false, trim: None, first_field_count: None, cur_pos: Position { byte: 0, line: 1, record: 0 }, first: false, seeked: false, eof: NotEof } }"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "01a3cd51-67a6-49b6-8234-726fe4b94b84",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Rust",
"language": "rust",
"name": "rust"
},
"language_info": {
"codemirror_mode": "rust",
"file_extension": ".rs",
"mimetype": "text/rust",
"name": "Rust",
"pygment_lexer": "rust",
"version": ""
}
},
"nbformat": 4,
"nbformat_minor": 5
}

View file

@ -1,7 +0,0 @@
#!/usr/bin/env python3
import sys
import pandas as pd
df = pd.read_csv(sys.stdin)
print(df.groupby("Dept", sort=False, as_index=False).Weekly_Sales.mean())

View file

@ -1,8 +0,0 @@
select
Dept,
avg(Weekly_Sales)
from
this
group by
Dept

123
src/commands.rs Normal file
View file

@ -0,0 +1,123 @@
use clap::{arg, ArgAction, Command};
// Generate command line options for the csv command
pub fn gen_csv_command() -> Command {
Command::new("csv")
.about("Read csv, output arrow stream")
.arg(arg!([path] "Path to CSV file"))
.arg(arg!(-d --delimiter <String> "Column delimiter. Assume ,").required(false))
.arg(
arg!(-i --stdin ... "Read from stdin")
.required(false)
.action(ArgAction::SetTrue),
)
.arg(arg!(-q --query <String> "Execute query on the file").required(false))
.arg(
arg!(-s --summary ... "Summarize the data")
.required(false)
.action(ArgAction::SetTrue),
)
.arg(
arg!(-t --text ... "Output text instead of binary")
.required(false)
.action(ArgAction::SetTrue),
)
.arg(arg!(-P --parquet <String> "Write output as a parquet file").required(false))
.arg(
arg!(-a --head ... "Print the header of the table")
.required(false)
.action(ArgAction::SetTrue),
)
}
// Generate command line options for the schema command
pub fn gen_schema_command() -> Command {
Command::new("schema")
.about("Several table schema related utilities")
.arg(
arg!(-i --stdin ... "Read from stdin")
.required(false)
.action(ArgAction::SetTrue),
)
.arg(arg!(-d --delimiter <String> "Column delimiter. Assume ,").required(false))
.arg(arg!(-n --name <String> "Table name").required(false))
.arg(arg!(-l --strlen <String> "Default length for string columns").required(false))
.arg(
arg!(-s --summary ... "Summarize the schema")
.required(false)
.action(ArgAction::SetTrue),
)
.arg(
arg!(-p --postgresql ... "Create a postgresql table with schema")
.required(false)
.action(ArgAction::SetTrue),
)
}
// Generate command line options for the sql command
pub fn gen_sql_command() -> Command {
Command::new("sql")
.about("Runs a sql statement on the file")
.arg(arg!(-d --delimiter <String> "Column delimiter. Assume ,").required(false))
.arg(arg!([statement] "SQL statement"))
.arg(
arg!(-t --text ... "Input text instead of binary")
.required(false)
.action(ArgAction::SetTrue),
)
.arg(arg!(-d --delimiter <String> "Column delimiter").required(false))
}
// Generate command line options for the rpq command
pub fn gen_rpq_command() -> Command {
Command::new("rpq")
.about("Read parquet file")
.arg(arg!([path] "Path to the parquet file"))
.arg(arg!(-q --query <String> "Execute query on the file").required(false))
.arg(
arg!(-s --summary ... "Summarize the data")
.required(false)
.action(ArgAction::SetTrue),
)
.arg(
arg!(-i --stdin ... "Read from stdin instead than from a file")
.required(false)
.action(ArgAction::SetTrue),
)
.arg(
arg!(-t --text ... "Output text instead of binary")
.required(false)
.action(ArgAction::SetTrue),
)
.arg(arg!(-P --parquet <String> "Write the result as a parquet file").required(false))
.arg(
arg!(-a --head ... "Print the header of the table")
.required(false)
.action(ArgAction::SetTrue),
)
}
// Generate command line options for the wpq command
pub fn gen_wpq_command() -> Command {
Command::new("wpq")
.about("Write to a paquet file")
.arg(arg!(-d --delimiter <String> "Column delimiter. Assume ,").required(false))
.arg(
arg!(-t --text ... "Input text instead of binary")
.required(false)
.action(ArgAction::SetTrue),
)
.arg(arg!([path] "Path to the new parquet file"))
}
// Generate command line options for the print command
pub fn gen_print_command() -> Command {
Command::new("print")
.about("Pretty prints the table")
.arg(arg!(-d --delimiter <String> "Column delimiter. Assume ,").required(false))
.arg(
arg!(-t --text ... "Inputs csv instead of binary")
.required(false)
.action(ArgAction::SetTrue),
)
}

150
src/handlers.rs Normal file
View file

@ -0,0 +1,150 @@
use crate::io;
use crate::schema;
use crate::sql;
use clap::ArgMatches;
use polars_lazy::prelude::LazyFrame;
// Handle csv command
pub fn handle_csv(matches: &ArgMatches) {
let delimiter = match matches.get_one::<String>("delimiter") {
Some(delimiter) => delimiter.as_bytes()[0],
None => b',',
};
let mut ldf = if matches.get_flag("stdin") {
io::load_csv_from_stdin(delimiter)
} else {
let path = matches
.get_one::<String>("path")
.expect("Please, provide a file");
io::read_csv(path.to_string(), delimiter)
};
if let Some(query) = matches.get_one::<String>("query") {
ldf = sql::execute(ldf, query);
}
if matches.get_flag("summary") {
let df = ldf.collect().expect("Could not collect");
println!("{:?}", df.describe(None));
} else if matches.get_flag("head") {
let df = ldf.fetch(5).expect("Could not fetch");
println!("{}", df)
} else {
if matches.get_flag("text") {
io::dump_csv_to_stdout(ldf);
} else {
if let Some(path) = matches.get_one::<String>("parquet") {
io::write_parquet(ldf, path.to_string());
} else {
io::write_ipc(ldf);
}
}
}
}
// Handle the SQL command
pub fn handle_sql(matches: &ArgMatches) {
let delimiter = match matches.get_one::<String>("delimiter") {
Some(delimiter) => delimiter.as_bytes()[0],
None => b',',
};
if let Some(statement) = matches.get_one::<String>("statement") {
let ldf = if matches.get_flag("text") {
io::load_csv_from_stdin(delimiter)
} else {
io::read_ipc()
};
let res = sql::execute(ldf, statement);
io::write_ipc(res);
} else {
io::write_ipc(io::read_ipc());
}
}
// Handle the print command
pub fn handle_print(matches: &ArgMatches) {
let delimiter = match matches.get_one::<String>("delimiter") {
Some(delimiter) => delimiter.as_bytes()[0],
None => b',',
};
let df = if matches.get_flag("text") {
io::load_csv_from_stdin(delimiter)
} else {
io::read_ipc()
};
println!("{}", df.collect().expect("Could not collect"));
}
// Handle the rpq command
pub fn handle_rpq(matches: &ArgMatches) {
let mut ldf = LazyFrame::default();
if matches.get_flag("stdin") {
ldf = io::load_parquet_from_stdin();
} else if let Some(path) = matches.get_one::<String>("path") {
ldf = io::read_parquet(path.to_string());
} else {
eprintln!("File not found or not reading from stdin")
}
if let Some(query) = matches.get_one::<String>("query") {
ldf = sql::execute(ldf, query);
}
if matches.get_flag("summary") {
let df = ldf.collect().expect("Could not collect");
println!("{:?}", df.describe(None));
} else if matches.get_flag("head") {
let df = ldf.fetch(5).expect("Could not fetch");
println!("{}", df)
} else {
if matches.get_flag("text") {
io::dump_csv_to_stdout(ldf);
} else {
if let Some(path) = matches.get_one::<String>("parquet") {
io::write_parquet(ldf, path.to_string());
} else {
io::write_ipc(ldf);
}
}
}
}
// Handle the wpq command
pub fn handle_wpq(matches: &ArgMatches) {
let delimiter = match matches.get_one::<String>("delimiter") {
Some(delimiter) => delimiter.as_bytes()[0],
None => b',',
};
if let Some(path) = matches.get_one::<String>("path") {
let ldf = if matches.get_flag("text") {
io::load_csv_from_stdin(delimiter)
} else {
io::read_ipc()
};
io::write_parquet(ldf, path.to_string());
} else {
eprintln!("Could now write to parquet");
}
}
// Handle the schema command
pub fn handle_schema(matches: &ArgMatches) {
let delimiter = match matches.get_one::<String>("delimiter") {
Some(delimiter) => delimiter.as_bytes()[0],
None => b',',
};
let ldf = if matches.get_flag("stdin") {
io::load_csv_from_stdin(delimiter)
} else {
io::read_ipc()
};
if matches.get_flag("summary") {
schema::print_schema(ldf);
} else if matches.get_flag("postgresql") {
let name = matches
.get_one::<String>("name")
.expect("Please provide a table name");
let strlen: u32 = match matches.get_one::<String>("strlen") {
Some(strlen) => strlen.parse::<u32>().unwrap(),
None => 128,
};
schema::print_create(ldf, name.as_str(), strlen);
}
}

135
src/io.rs
View file

@ -1,68 +1,105 @@
use polars::frame::DataFrame;
use polars::prelude::*;
use std::fs;
use polars_io::prelude::*;
use polars_lazy::prelude::*;
use std::io;
use std::io::Read;
use std::path::PathBuf;
/// Read CSV format from stdin and return a Polars DataFrame
pub fn load_csv_from_stdin() -> DataFrame {
let mut buffer = String::new();
let _res: () = match io::stdin().read_to_string(&mut buffer) {
/// Read CSV file
pub fn read_csv(path: String, delimiter: u8) -> LazyFrame {
LazyCsvReader::new(path)
.with_delimiter(delimiter)
.with_infer_schema_length(None)
.finish()
.expect("Could not load file")
}
/// Read parquet and return a Polars LazyFrame
pub fn read_parquet(path: String) -> LazyFrame {
LazyFrame::scan_parquet(path, ScanArgsParquet::default()).expect("Could not read parquet file")
}
/// Read IPC setream
pub fn read_ipc() -> LazyFrame {
let mut buffer = Vec::new();
let _res: () = match io::stdin().lock().read_to_end(&mut buffer) {
Ok(_ok) => (),
Err(_e) => (),
};
let cursor = io::Cursor::new(buffer.as_bytes());
let df = match CsvReader::new(cursor).finish() {
Ok(df) => df,
Err(_e) => DataFrame::default(),
let cursor = io::Cursor::new(buffer);
match IpcStreamReader::new(cursor).finish() {
Ok(df) => df.lazy(),
Err(_e) => LazyFrame::default(),
}
}
/// Read CSV format from stdin and return a Polars DataFrame
pub fn load_csv_from_stdin(delimiter: u8) -> LazyFrame {
let mut buffer = Vec::new();
let _res: () = match io::stdin().lock().read_to_end(&mut buffer) {
Ok(_ok) => (),
Err(_e) => (),
};
df
let cursor = io::Cursor::new(buffer);
match CsvReader::new(cursor).with_delimiter(delimiter).finish() {
Ok(df) => df.lazy(),
Err(_e) => LazyFrame::default(),
}
}
/// Read CSV format from stdin and return a Polars DataFrame
pub fn load_parquet_from_stdin() -> LazyFrame {
let mut buffer = Vec::new();
let _res: () = match io::stdin().lock().read_to_end(&mut buffer) {
Ok(_ok) => (),
Err(_e) => (),
};
let cursor = io::Cursor::new(buffer);
match ParquetReader::new(cursor).finish() {
Ok(df) => df.lazy(),
Err(_e) => LazyFrame::default(),
}
}
/// Write to IPC steram
pub fn write_ipc(df: LazyFrame) {
IpcStreamWriter::new(io::stdout().lock())
.finish(&mut df.collect().expect("Could not collect dataframe"))
.expect("Could not write to stream");
}
/// Take a Polars Dataframe and write it as CSV to stdout
pub fn dump_csv_to_stdout(df: &mut DataFrame) {
let _res: () = match CsvWriter::new(io::stdout().lock()).finish(df) {
pub fn dump_csv_to_stdout(ldf: LazyFrame) {
let _res: () = match CsvWriter::new(io::stdout().lock())
.finish(&mut ldf.collect().expect("Could not collect"))
{
Ok(_ok) => (),
Err(_e) => (),
};
}
/// Read parquet and return a Polars DataFrame
pub fn read_parquet(path: String) -> DataFrame {
let file = fs::File::open(path).expect("Could not open file");
let df = match ParquetReader::new(file).finish() {
Ok(df) => df,
Err(e) => {
eprintln!("{e}");
DataFrame::default()
}
};
df
/// Write a Polars DataFrame to Parquet
/// Not yet supported in standard executor
pub fn sink_parquet(ldf: LazyFrame, path: String) {
// Selected compression not implemented yet
let mut p = PathBuf::new();
p.push(path);
ldf.sink_parquet(
p,
ParquetWriteOptions {
compression: ParquetCompression::Snappy,
statistics: true,
row_group_size: None,
data_pagesize_limit: None,
maintain_order: false,
},
)
.expect("Could not save");
}
/// Write a Polars DataFrame to Parquet
pub fn write_parquet(
mut df: DataFrame,
path: String,
compression: String,
statistics: bool,
chunksize: Option<usize>,
) {
pub fn write_parquet(ldf: LazyFrame, path: String) {
// Selected compression not implemented yet
let mut _file = match fs::File::create(path) {
Ok(mut file) => {
let mut w = ParquetWriter::new(&mut file);
if statistics {
w = w.with_statistics(statistics);
}
if chunksize.unwrap_or(0) > 0 {
w = w.with_row_group_size(chunksize);
}
let _r = match w.finish(&mut df) {
Ok(_r) => (),
Err(e) => eprintln!("{e}"),
};
}
Err(e) => eprintln!("{e}"),
};
let mut file = std::fs::File::create(path).unwrap();
ParquetWriter::new(&mut file)
.finish(&mut ldf.collect().expect("Could not collect"))
.unwrap();
}

View file

@ -1,57 +1,44 @@
mod commands;
mod handlers;
mod io;
mod schema;
mod sql;
use clap::{arg, command, Command};
use clap::command;
fn main() {
// Commands definition
let matches = command!()
.subcommand(
Command::new("sql")
.about("Runs a sql statement on the file")
.arg(arg!([statement] "SQL statement"))
.arg(arg!(-d --delimiter <String> "Column delimiter").required(false)),
)
.subcommand(Command::new("print").about("Pretty prints the table"))
.subcommand(
Command::new("rpq")
.about("Read parquet file")
.arg(arg!([path] "Path to the parquet file")),
)
.subcommand(
Command::new("wpq")
.about("Write to a paquet file")
.arg(arg!([path] "Path to the new parquet file