ClickBench
Crate
ClickBench | Crate | |
---|---|---|
71 | 6 | |
571 | 3,957 | |
3.2% | 0.5% | |
9.0 | 9.9 | |
2 days ago | 7 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
-
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.
-
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.
-
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.)
-
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
-
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/
-
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
Crate
- FLaNK AI - 01 April 2024
-
Creating an advanced search engine with PostgreSQL
I'm wondering if CrateDB [https://github.com/crate/crate] could fit your use case.
It's a relational SQL database which aims for compatibility with PostgreSQL. Internally it uses Lucene as a storage and such can offer fulltext functionality which is exposed via MATCH.
-
Distributed query execution in CrateDB: What you need to know
A logical execution plan does not take into account the information about data distribution. CrateDB is a distributed database and data is sharded: a table can be split into many parts - so-called shards. Shards can be independently replicated and moved from one node to another. The number of shards a table can have is specified at the time the table is created.
- Parser generators vs. handwritten parsers: surveying major languages in 2021
-
Querying time series data with SQL: examples
PD: If you liked this post... We'd really appreciate a ⭐️ in Github!
-
What is CrateDB? 🤔 FAQ
But there's nothing better than trying things by yourself... So Download CrateDB, experiment, and tell us what you think! 😁
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.
Presto - The official home of the Presto distributed SQL query engine for big data
duckdb - DuckDB is an in-process SQL OLAP Database Management System
OrientDB - OrientDB is the most versatile DBMS supporting Graph, Document, Reactive, Full-Text and Geospatial models in one Multi-Model product. OrientDB can run distributed (Multi-Master), supports SQL, ACID Transactions, Full-Text indexing and Reactive Queries.
ClickHouse - ClickHouse® is a free analytics DBMS for big data
MapDB - MapDB provides concurrent Maps, Sets and Queues backed by disk storage or off-heap-memory. It is a fast and easy to use embedded Java database engine.
hosts - 🔒 Consolidating and extending hosts files from several well-curated sources. Optionally pick extensions for porn, social media, and other categories.
jOOQ - jOOQ is the best way to write SQL in Java
TablePlus - TablePlus macOS issue tracker
Flyway - Flyway by Redgate • Database Migrations Made Easy.
clickhouse-bulk - Collects many small inserts to ClickHouse and send in big inserts
sql2o - sql2o is a small library, which makes it easy to convert the result of your sql-statements into objects. No resultset hacking required. Kind of like an orm, but without the sql-generation capabilities. Supports named parameters.