tsbs
timescale-analytics
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tsbs | timescale-analytics | |
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76 | 8 | |
1,201 | 330 | |
2.1% | 4.9% | |
1.9 | 6.2 | |
8 days ago | about 1 month ago | |
Go | Rust | |
MIT License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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tsbs
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Fuzz Testing Is the Best Thing to Happen to Our Application Tests
1. correctness: from small units tests to relatively complex integrations tests. they typically populate a test database and query it via various interfaces, such as REST or the Postgres protocol. we use Azure Pipelines to execute them - testing in MacoOS, Linux (both Intel and ARM) and Windows.
2. performance: we tend to use the TSBS project for most of our performance testing and profiling. fun fact: we actually had to patch it as the vanilla TSBS was a bottleneck in some tests. Sadly, the PR with the improvements is still not merged: https://github.com/timescale/tsbs/pull/186
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MongoDB Time Series Benchmark and Review
As usual, we use the industry standard Time Series Benchmark Suite (TSBS) as the benchmark tool. Unfortunately, TSBS upstream does not support MongoDB time series collections.
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Show HN: QuestDB with Python, Pandas and SQL in a Jupyter notebook – no install
yes correct - although Clickhouse is more of an OLAP database. Timescale is built on top of Postgres, while QuestDB is built from scratch with Postgres wire compatibility. You can run benchmarks on https://github.com/timescale/tsbs
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Streaming data storage
According their benchmark it is really fast.
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Ingesting with CrateDB
We used the nodeIngestBench for all the benchmarking. It is a multi-process Node.js script that runs high-performance ingest benchmarks on CrateDB. It uses a data model that was adapted from Timescale’s Time Series Benchmark Suite (TSBS). One thing that we want to make clear is that nodeIngestBench is a write benchmark. The data structure that it creates is unsuitable for any performance-indicative reading tests because of its high cardinality (due to random data) and no partitioning.
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4Bn rows/sec query benchmark: Clickhouse vs QuestDB vs Timescale
In order to make the benchmark easily reproducible, we're going to use TSBS benchmark utilities to generate the data. We'll be using so-called IoT use case:
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DeWitt Clause, or Can You Benchmark %DATABASE% and Get Away With It
Also, some open-source vendors collaboratively maintain benchmarking suites such as Time Series Benchmark Suite to help choose the best tools for particular workloads.
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4Bn rows/SEC query benchmark: ClickHouse vs. QuestDB vs. Timescale
Last year we released QuestDB 6.0 and achieved an ingestion rate of 1.4 million rows per second (per server). We compared those results to popular open source databases [1] and explained how we dealt with out of order ingestion under the hood while keeping the underlying storage model read-friendly. Since then, we focused our efforts on making queries faster, in particular filter queries with WHERE clauses. To do so, we once again decided to make things from scratch and built a JIT (Just-in-Time) compiler for SQL filters, with tons of low-level optimisations such as SIMD. We then parallelized the query execution to improve the execution time even further. In this blog post, we first look at some benchmarks against Clickhouse and TimescaleDB, before digging deeper in how this all works within QuestDB's storage model. Once again, we use the Time Series Benchmark Suite (TSBS) [2], developed by TimescaleDB,: it is an open source and reproducible benchmark.
We'd love to get your feedback!
This table schema: https://github.com/timescale/tsbs/blob/bcc00137d72d889e6059e...
...seems like a quite odd way to store time-series in ClickHouse. If I understood that code correctly (and I am really not sure), they partition their data by some tag value (the first one in a list?) instead of time, which is what timescaledb afaik partitions by. Of course that query filtering by timerange is going to be slower than usual. Whether that makes sense depends on your usecase.
timescale-analytics
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Timescale raises $110M Series C
Hi! So the team is over 100 at this point, but engineering effort is spread across multiple products at this point.
The core timescaledb repo [0] has 10-15 primary engineers (although we are aggressively hiring for database internal engineers), with a few others working on DB hyperfunctions and our function pipelining [1] in a separate extension [2]. I think generally the set of folks who contribute to low-level database internals in C is just smaller than other type of projects.
We also have our promscale product [3], which is our observability backend powered by SQL & TimescaleDB.
And then there is Timescale Cloud, which is obviously a large engineering effort (most of which does not happen in public repos).
And we are hiring. Fully remote & global.
https://www.timescale.com/careers
[0] https://github.com/timescale/timescaledb
[1] https://www.timescale.com/blog/function-pipelines-building-f...
[2] https://github.com/timescale/timescaledb-toolkit
[3] https://github.com/timescale/promscale ; https://github.com/timescale/tobs
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Function pipelines: Building functional programming into PostgreSQL
(NB: Post author here)
This is in the TimescaleDB Toolkit extension [1] which is licensed under our community license for now and it's not available on DO. It is available on our cloud service fully managed. You can also install it and run it for free yourself.
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How percentile approximation works (and why it's more useful than averages)
NB: Post author here.
Thanks for sharing! Hadn't heard of that algorithm, have seen a number of other ones out there, we chose a couple that we knew about / were requested by users. (And we are open to more user requests if folks want to use other ones! https://github.com/timescale/timescaledb-toolkit and open an issue!)
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How PostgreSQL aggregation works and how it inspired our hyperfunctions’ design
Absolutely! We're actually developing a lot of that: https://github.com/timescale/timescaledb-toolkit/tree/main/d...
A number of the things you're looking for we've done experimentally and we'll be stabilizing over the next few releases. So we'd love some feedback while we're still able to futz with the API without making breaking changes.
But the two you're asking about are, I think, going to be covered by hyperloglog (we just reimplemented the internals with HLL++) and stats_agg family of functions, which have both 1D (which will give you avg, stddev, variance, etc) and 2D (co-variance, slope, intercept, x-intercept etc as well as all the 1D functions).
Would also love issues if you think we're missing other stuff, going to be generalizing this and want to make it useful for folks.
(NB: Post author here.)
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TimescaleDB Raises $40M
Fair point about adaptive chunking. You sound like a long-term user!
There is always a trade-off between getting features to users quickly to experiment and incrementally improve, versus doing it always very conservatively.
When we launched adaptive chunking (introduced in 0.11, deprecated in 1.2), we explicitly marked it as beta and default off, to hopefully reflect that. [1]
The approach we are now taking with Timescale Analytics [2] is to have an explicit distinction between experimental features (which will be part of a distinct"experimental" schema in the database, and must be expressly turned on with appropriate warnings) and stable features. Hopefully this can help find a good balance between stability and velocity, but feedback welcome!
[1] https://github.com/timescale/timescaledb/releases/tag/0.11.0
[2] https://github.com/timescale/timescale-analytics/tree/main/e...
What are some alternatives?
QuestDB - An open source time-series database for fast ingest and SQL queries
TimescaleDB - An open-source time-series SQL database optimized for fast ingest and complex queries. Packaged as a PostgreSQL extension.
cql-proxy - A client-side CQL proxy/sidecar.
orioledb - OrioleDB – building a modern cloud-native storage engine (... and solving some PostgreSQL wicked problems)  🇺🇦
dbt-clickhouse - The Clickhouse plugin for dbt (data build tool)
Elasticsearch - Free and Open, Distributed, RESTful Search Engine
promscale - [DEPRECATED] Promscale is a unified metric and trace observability backend for Prometheus, Jaeger and OpenTelemetry built on PostgreSQL and TimescaleDB.
duckdb - DuckDB is an in-process SQL OLAP Database Management System
ClickHouse - ClickHouse® is a free analytics DBMS for big data
VictoriaMetrics - VictoriaMetrics: fast, cost-effective monitoring solution and time series database