ClickBench
QuestDB
ClickBench | QuestDB | |
---|---|---|
72 | 311 | |
577 | 13,535 | |
4.2% | 1.1% | |
9.0 | 9.7 | |
7 days ago | 3 days ago | |
HTML | Java | |
GNU General Public License v3.0 or later | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
ClickBench
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Umbra: A Disk-Based System with In-Memory Performance [pdf]
Benchmarks: https://benchmark.clickhouse.com
So definitely compared against PostgreSQL, MariaDB it is significantly faster.
On par with lower-end Snowflake.
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Loading a trillion rows of weather data into TimescaleDB
TimescaleDB primarily serves operational use cases: Developers building products on top of live data, where you are regularly streaming in fresh data, and you often know what many queries look like a priori, because those are powering your live APIs, dashboards, and product experience.
That's different from a data warehouse or many traditional "OLAP" use cases, where you might dump a big dataset statically, and then people will occasionally do ad-hoc queries against it. This is the big weather dataset file sitting on your desktop that you occasionally query while on holidays.
So it's less about "can you store weather data", but what does that use case look like? How are the queries shaped? Are you saving a single dataset for ad-hoc queries across the entire dataset, or continuously streaming in new data, and aging out or de-prioritizing old data?
In most of the products we serve, customers are often interested in recent data in a very granular format ("shallow and wide"), or longer historical queries along a well defined axis ("deep and narrow").
For example, this is where the benefits of TimescaleDB's segmented columnar compression emerges. It optimizes for those queries which are very common in your application, e.g., an IoT application that groups by or selected by deviceID, crypto/fintech analysis based on the ticker symbol, product analytics based on tenantID, etc.
If you look at Clickbench, what most of the queries say are: Scan ALL the data in your database, and GROUP BY one of the 100 columns in the web analytics logs.
- https://github.com/ClickHouse/ClickBench/blob/main/clickhous...
There are almost no time-predicates in the benchmark that Clickhouse created, but perhaps that is not surprising given it was designed for ad-hoc weblog analytics at Yandex.
So yes, Timescale serves many products today that use weather data, but has made different choices than Clickhouse (or things like DuckDB, pg_analytics, etc) to serve those more operational use cases.
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Variant in Apache Doris 2.1.0: a new data type 8 times faster than JSON for semi-structured data analysis
We tested with 43 Clickbench SQL queries. Queries on the Variant columns are about 10% slower than those on pre-defined static columns, and 8 times faster than those on JSON columns. (For I/O reasons, most cold runs on JSONB data failed with OOM.)
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Fair Benchmarking Considered Difficult (2018) [pdf]
I have a project dedicated to this topic: https://github.com/ClickHouse/ClickBench
It is important to explain the limitations of a benchmark, provide a methodology, and make it reproducible. It also has to be simple enough, otherwise it will not be realistic to include a large number of participants.
I'm also collecting all database benchmarks I could find: https://github.com/ClickHouse/ClickHouse/issues/22398
- ClickBench – A Benchmark for Analytical DBMS
- FLaNK Stack 05 Feb 2024
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Why Postgres RDS didn't work for us
Indeed, ClickHouse results were run on an older instance type of the same family and size (c5.4xlarge for ClickHouse and c6a.4xlarge for Timescale), so if anything ClickHouse results are at a slight disadvantage.
This is an open source benchmark - we'd love contributions from Timescale enthusiasts if we missed something: https://github.com/ClickHouse/ClickBench/
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Show HN: Stanchion – Column-oriented tables in SQLite
Interesting project! Thank you for open sourcing and sharing. Agree that local and embedded analytics are an increasing trend, I see it too.
A couple of questions:
* I’m curious what the difficulties were in the implementation. I suspect it is quite a challenge to implement this support in the current SQLite architecture, and would curious to know which parts were tricky and any design trade-off you were faced with.
* Aside from ease-of-use (install extension, no need for a separate analytical database system), I wonder if there are additional benefits users can anticipate resulting from a single system architecture vs running an embedded OLAP store like DuckDB or clickhouse-local / chdb side-by-side with SQLite? Do you anticipate performance or resource efficiency gains, for instance?
* I am also curious, what the main difficulty with bringing in a separate analytical database is, assuming it natively integrates with SQLite. I may be biased, but I doubt anything can approach the performance of native column-oriented systems, so I'm curious what the tipping point might be for using this extension vs using an embedded OLAP store in practice.
Btw, would love for you or someone in the community to benchmark Stanchion in ClickBench and submit results! (https://github.com/ClickHouse/ClickBench/)
Disclaimer: I work on ClickHouse.
- ClickBench: A Benchmark for Analytical Databases
- DuckDB performance improvements with the latest release
QuestDB
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How to Forecast Air Temperatures with AI + IoT Sensor Data
If your data lacks uniform time intervals between consecutive entries, QuestDB offers a solution by allowing you to sample your data. After that, MindsDB facilitates creating, training, and deploying your time-series models.
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Normalizing Grafana charts with window functions
If you're interested in that functionality or have any other feedback, please drop by our open source repository or community Slack and let us know.
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How to increase Grafana refresh rate frequency
QuestDB is a high-performance time series database with SQL analytics that can power through market data ingestion and analysis. It's open source and integrates well with the tools and languages you use. Check us out!
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Building a faster hash table for high performance SQL joins
Looks like full keys are always compared if hash codes test equal, which is what I'd expect. For example: https://github.com/questdb/questdb/blob/master/core/src/main...
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K3s Traefik Ingress - configured for your homelab!
But of course, I want to run a QuestDB instance on my node, which uses two additional TCP ports for Influx Line Protocol (ILP) and Pgwire communication with the database. So how can I expose these extra ports on my node and route traffic to the QuestDB container running inside of k3s?
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Annotations in Kubernetes Operator Design
In this post, I will detail a way in which I recently used annotations while writing an operator for my company's product, QuestDB. Hopefully this will give you an idea of how you can incorporate annotations into your own operators to harness their full potential.
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Is all data time-series data?
QuestDB is an open source, high performance time series database. With its massive ingestion throughput speeds and cost effective operation, QuestDB reduces infrastructure costs and helps you overcome tricky ingestion bottlenecks. Thanks for reading!
- questdb: NEW Data - star count:12960.0
What are some alternatives?
starrocks - StarRocks, a Linux Foundation project, is a next-generation sub-second MPP OLAP database for full analytics scenarios, including multi-dimensional analytics, real-time analytics, and ad-hoc queries. InfoWorld’s 2023 BOSSIE Award for best open source software.
TDengine - TDengine is an open source, high-performance, cloud native time-series database optimized for Internet of Things (IoT), Connected Cars, Industrial IoT and DevOps.
duckdb - DuckDB is an in-process SQL OLAP Database Management System
arctic - High performance datastore for time series and tick data
ClickHouse - ClickHouse® is a free analytics DBMS for big data
hosts - 🔒 Consolidating and extending hosts files from several well-curated sources. Optionally pick extensions for porn, social media, and other categories.
SQLAlchemy - The Database Toolkit for Python
TablePlus - TablePlus macOS issue tracker
VictoriaMetrics - VictoriaMetrics: fast, cost-effective monitoring solution and time series database
clickhouse-bulk - Collects many small inserts to ClickHouse and send in big inserts
tsbs - Time Series Benchmark Suite, a tool for comparing and evaluating databases for time series data