kafka-delta-ingest
db-benchmark
kafka-delta-ingest | db-benchmark | |
---|---|---|
6 | 91 | |
325 | 320 | |
3.4% | 0.0% | |
7.4 | 0.0 | |
18 days ago | 10 months ago | |
Rust | R | |
Apache License 2.0 | Mozilla Public License 2.0 |
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kafka-delta-ingest
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Using rust for DE activities?
Rust can offer incredible cost savings when you can use it in place of spark to interact with your delta lake. One such project was kafka-delta-ingest. The developers were able to reduce the cost of running the pipeline by over 90%. However, most of this stuff is still very experimental and not ready for production but you will definitely be seeing more projects like this just based on how much money can be saved.
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Which lakehouse table format do you expect your organization will be using by the end of 2023?
This independence from a catalog allows for path based reads and writes. This is handy when writing from Kafka directly to Delta Lake for the first layer of ingestion. You don’t need a catalog (or even Spark). https://github.com/delta-io/kafka-delta-ingest/tree/main/src
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Streaming Data and Postgres
As far as I know no. You certainly could use events on a streaming ledger like Kafka or Redpanda and then store to delta with https://github.com/delta-io/kafka-delta-ingest and process them with all the gis goodness of spark. However, this is fairly complicated and much different from a simple postgis drop in replacement. There are specialized meaning faster and more efficient systems out there for specialized tasks such as geo fencing in real-time
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Rust is showing a lot of promise in the DataFrame / tabular data space
kafka-delta-ingest is a good project to get streaming data into a Delta Lake. Here's a great talk on the topic.
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process millions of events per sec
What about https://github.com/delta-io/kafka-delta-ingest?
- Exactly once delivery from Kafka to Delta Lake with Rust
db-benchmark
- Database-Like Ops Benchmark
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Polars
Real-world performance is complicated since data science covers a lot of use cases.
If you're just reading a small CSV to do analysis on it, then there will be no human-perceptible difference between Polars and Pandas. If you're reading a larger CSV with 100k rows, there still won't be much of a perceptible difference.
Per this (old) benchmark, there are differences once you get into 500MB+ territory: https://h2oai.github.io/db-benchmark/
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DuckDB performance improvements with the latest release
I do think it was important for duckdb to put out a new version of the results as the earlier version of that benchmark [1] went dormant with a very old version of duckdb with very bad performance, especially against polars.
[1] https://h2oai.github.io/db-benchmark/
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Show HN: SimSIMD vs. SciPy: How AVX-512 and SVE make SIMD cleaner and ML faster
https://news.ycombinator.com/item?id=33270638 :
> Apache Ballista and Polars do Apache Arrow and SIMD.
> The Polars homepage links to the "Database-like ops benchmark" of {Polars, data.table, DataFrames.jl, ClickHouse, cuDF, spark, (py)datatable, dplyr, pandas, dask, Arrow, DuckDB, Modin,} but not yet PostgresML? https://h2oai.github.io/db-benchmark/ *
LLM -> Vector database: https://en.wikipedia.org/wiki/Vector_database
/? inurl:awesome site:github.com "vector database"
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Pandas vs. Julia – cheat sheet and comparison
I agree with your conclusion but want to add that switching from Julia may not make sense either.
According to these benchmarks: https://h2oai.github.io/db-benchmark/, DF.jl is the fastest library for some things, data.table for others, polars for others. Which is fastest depends on the query and whether it takes advantage of the features/properties of each.
For what it's worth, data.table is my favourite to use and I believe it has the nicest ergonomics of the three I spoke about.
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Any faster Python alternatives?
Same. Numba does wonders for me in most scenarios. Yesterday I've discovered pola-rs and looks like I will add it to the stack. It's API is similar to pandas. Have a look at the benchmarks of cuDF, spark, dask, pandas compared to it: Benchmarks
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Pandas 2.0 (with pyarrow) vs Pandas 1.3 - Performance comparison
The syntax has similarities with dplyr in terms of the way you chain operations, and it’s around an order of magnitude faster than pandas and dplyr (there’s a nice benchmark here). It’s also more memory-efficient and can handle larger-than-memory datasets via streaming if needed.
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Pandas v2.0 Released
If interested in benchmarks comparing different dataframe implementations, here is one:
https://h2oai.github.io/db-benchmark/
- Database-like ops benchmark
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Python "programmers" when I show them how much faster their naive code runs when translated to C++ (this is a joke, I love python)
Bad examples. Both numpy and pandas are notoriously un-optimized packages, losing handily to pretty much all their competitors (R, Julia, kdb+, vaex, polars). See https://h2oai.github.io/db-benchmark/ for a partial comparison.
What are some alternatives?
delta-rs - A native Rust library for Delta Lake, with bindings into Python
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
dipa - dipa makes it easy to efficiently delta encode large Rust data structures.
datafusion - Apache DataFusion SQL Query Engine
kafka-rust - Rust client for Apache Kafka
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
rust-rdkafka - A fully asynchronous, futures-based Kafka client library for Rust based on librdkafka
databend - 𝗗𝗮𝘁𝗮, 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 & 𝗔𝗜. Modern alternative to Snowflake. Cost-effective and simple for massive-scale analytics. https://databend.com
flowgger - A fast data collector in Rust
sktime - A unified framework for machine learning with time series
arrow2 - Transmute-free Rust library to work with the Arrow format
DataFramesMeta.jl - Metaprogramming tools for DataFrames