Databend is an open source elastic and reliable Modern Cloud Data Warehouse, it offers blazing fast query and combines elasticity, simplicity, low cost of the cloud, built to make the Data Cloud easy.
Below is a list of some major changes that we don't want you to miss.
- databend-query(expressions): add try_cast function by @sundy-li, (#4794)
- common-functions: support cast variant to other data types by @b41sh, (#4787)
- common-functions: support
REGEXP_SUBSTRfunction by @nange, (#4771)
- databend-query: support show table status by @TCeason, (#4757)
- common-functions: support semi-structured function
GET/GET_IGNORE_CASE/GET_PATHby @b41sh, (#4684)
- databend-query: pass parameter from query to functions by @Veeupup, (#4805)
- databend-query(mysql_handler): add more federated command for some old drivers by @BohuTANG, (#4809)
- databend-query(compact): add transform compact by @sundy-li, (#4784)
- databend-query(storage): show fuse engine table status by @dantengsky, (#4786)
- databend-query(mysql_handler): sqlalchemy execute work by @BohuTANG, (#4774)
- databend-query(fuse): limit push down respect orders by @sundy-li, (#4818)
- common-meta(state_machine): rename table should keep table_id nochange by @ariesdevil, (#4838)
- common-building: try persist credits at build time by @PsiACE, (#4791)
select *shouldn't return results by @xudong963, (#4796)
Let's learn a weekly tip from Databend.
Databend Performance Data Collection and Visualization
Late last week, we proudly announced the https://perf.databend.rs/. This is a website for monitoring the performance of Databend's nightly releases.
All benchmarks are currently running on an Amazon EC2 server of size
c5n.9xlarge, with 36 vCPUs and 96 GiB of memory, and Intel Xeon Platinum 8000 processors.
The current benchmarks consists of:
- A set of numerical computation SQLs for evaluating the performance of in-memory vectorization engines, based on Databend's numbers table function providing ten billions data.
- A common set of SQLs for air traffic analysis, based on the publicly available OnTime dataset from the U.S. Department of Transportation, 60.8 GB of CSV, 202687654 records.
To view the source code, please visit GitHub - datafuselabs/databend-perf:
- collector: stores daily performance data for each nightly release.
- benchmarks: contains the benchmark suite defined by the yaml format.
You can check the changelogs of Databend nightly to learn about our latest developments.
Please join the DatafuseLabs Community if you are interested in Databend.
We are looking forward to seeing you try our code. We have a strong team behind you to ensure a smooth experience in trying our code for your projects. If you are a hacker passionate about database internals, feel free to play with our code.
You can submit issues for any problems you find. We also highly appreciate any of your pull requests.