[PERF] Add a parallel local CSV reader

[desmondcheongzx]

Adds a parallel CSV reader to speed up ingestion of CSV. The approach adapts some ideas laid out in [1], but the majority of performance gains came from the use of buffer pools to minimize memory allocations.

Some performance numbers

We consider a simple case of reading and performing .collect() on a CSV file with 10^8 rows of 9 fields: 3 string fields, 5 int64 fields, and 1 double field. This file is roughly 5GB in size.

Non-native executor:                 38.71140212500177s
Non-native executor, new CSV reader: 7.432862582994858s
Native executor:                     44.55550079200475s
Native executor, new CSV reader:     4.117344291880727s

This represents a roughly 10x speedup on CSV reads for the native executor.

Followup work

• The schema is currently taken from the convert options, but we should also perform schema inference.
• We need to add better estimators for record size, either via sampling, or by keeping track of stats as we go.
• Currently, for each read, the reader creates a buffer pool for reading CSV records plus a pool of slabs for reading the CSV file. We might need to change these to per-process pools to avoid high memory pressure on concurrent reads. However care must be taken otherwise it's possible for use to deadlock.

[1]: Ge, Chang et al. “Speculative Distributed CSV Data Parsing for Big Data Analytics.” Proceedings of the 2019 International Conference on Management of Data (2019).

[codspeed-hq]

CodSpeed Performance Report

Merging #2772 will degrade performances by 33.78%

_{Comparing desmondcheongzx:local-csv-reader-experiment (7b40f23) with main (cad9168)}

Summary

⚡ 2 improvements
❌ 1 regressions
✅ 13 untouched benchmarks

⚠️ Please fix the performance issues or acknowledge them on CodSpeed.

Benchmarks breakdown

	Benchmark	main	desmondcheongzx:local-csv-reader-experiment	Change
❌	test_count[1 Small File]	16.5 ms	24.9 ms	-33.78%
⚡	test_explain[100 Small Files]	52.8 ms	39.9 ms	+32.33%
⚡	test_show[100 Small Files]	597.4 ms	355.8 ms	+67.91%

[universalmind303]

won't this break on quoted columns with a newline in them?

c0,c1,quoted_column
1,true,"this is \n quoted"

[desmondcheongzx]

Yeah that's correct. I left some followup TODOs below, but we haven't handled this case yet.

Some ideas we tossed around:

• Do speculative parsing (assume each chunk could start in quoted or unquoted case, then prune the possibility as we go).
• Check if we're in a file without quoted fields/let users specify this. In the happy path without quoted fields we can just rip through it. If not use speculative parsing.
• We can even imagine other edge cases that we might not handle correctly. In those cases simply fallback to an existing mature csv reader.

[universalmind303]

you should be able to do something similar to what I did in JSON here to get the estimated sizes

[colin-ho]

I think you might deadlock here, e.g. if your channel is full and all threads are trying to send chunks, they will all block. For parquet, we capped it to expected_num_chunks, i.e. the total amount of chunks that will ever be read. See: https://github.com/Eventual-Inc/Daft/pull/2620/files#diff-f65cce5db7fd92cf9fe1cc38ebd495165b7ac59444a91f0c93c4c4c027089429R392-R400

[colin-ho]

Oh and also (maybe you have this as a todo but just in case), the ordering should also be preserved, (for the streaming executor, there's a maintain_order flag that indicates if order needs to be preserved, but the non-streaming one requires order). I don't think into_par_iter.for_each will preserve it but correct me if im wrong (I believe it only preserves order if it ends in a collect rayon-rs/rayon#551)