reddit_mining
pysimdjson
reddit_mining | pysimdjson | |
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4 | 6 | |
11 | 629 | |
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2.6 | 5.3 | |
10 months ago | 3 months ago | |
HTML | Python | |
Creative Commons Zero v1.0 Universal | GNU General Public License v3.0 or later |
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reddit_mining
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Analyzing multi-gigabyte JSON files locally
zstd decompression should almost always be very fast. It's faster to decompress than DEFLATE or LZ4 in all the benchmarks that I've seen.
you might be interested in converting the pushshift data to parquet. Using octosql I'm able to query the submissions data (from the begining of reddit to Sept 2022) in about 10 min
https://github.com/chapmanjacobd/reddit_mining#how-was-this-...
Although if you're sending the data to postgres or BigQuery you can probably get better query performance via indexes or parallelism.
- reddit_mining - List of all Subreddits
- Show HN: List of All Subreddits
- Top 50k Subreddits
pysimdjson
- Analyzing multi-gigabyte JSON files locally
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I Use C When I Believe in Memory Safety
Its magic function wrapping comes at a cost, trading ease of use for runtime performance. When you have a single C++ function to call that will run for a "long" time, pybind all the way. But pysimdjson tends to call a single function very quickly, and the overhead of a single function call is orders of magnitude slower than with cython when being explit with types and signatures. Wrap a class in pybind11 and cython and compare the stack trace between the two, and the difference is startling.
Ex: https://github.com/TkTech/pysimdjson/issues/73
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Processing JSON 2.5x faster than simdjson with msgspec
simdjson
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[package-find] lsp-bridge
You are aware of simdjson being available in python if you really need some json crunching, albeit json module in Python is implemented in C itself, so I don't think understand why do you think Python is slow there?
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The fastest tool for querying large JSON files is written in Python (benchmark)
json: 113.79130696877837 ms
While `orjson`, is faster than `ujson`/`json` here, it's only ~6% faster (in this benchmark). `simdjson` and `msgspec` (my library, see https://jcristharif.com/msgspec/) are much faster due to them avoiding creating PyObjects for fields that are never used.
If spyql's query engine can determine the fields it will access statically before processing, you might find using `msgspec` for JSON gives a nice speedup (it'll also type check the JSON if you know the type of each field). If this information isn't known though, you may find using `pysimdjson` (https://pysimdjson.tkte.ch/) gives an easy speed boost, as it should be more of a drop-in for `orjson`.
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How I cut GTA Online loading times by 70%
I don't think JSON is really the problem - parsing 10MB of JSON is not so slow. For example, using Python's json.load takes about 800ms for a 47MB file on my system, using something like simdjson cuts that down to ~70ms.
What are some alternatives?
json-streamer - A fast streaming JSON parser for Python that generates SAX-like events using yajl
orjson - Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy
json-buffet
cysimdjson - Very fast Python JSON parsing library
semi_index - Implementation of the JSON semi-index described in the paper "Semi-Indexing Semi-Structured Data in Tiny Space"
ultrajson - Ultra fast JSON decoder and encoder written in C with Python bindings
jq-zsh-plugin - jq zsh plugin
Fast JSON schema for Python - Fast JSON schema validator for Python.
octosql - OctoSQL is a query tool that allows you to join, analyse and transform data from multiple databases and file formats using SQL.
lupin is a Python JSON object mapper - Python document object mapper (load python object from JSON and vice-versa)
zsv - zsv+lib: tabular data swiss-army knife CLI + world's fastest (simd) CSV parser
PyValico - Small python wrapper around https://github.com/rustless/valico