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Comments (6)

cjkbjhb avatar cjkbjhb commented on May 13, 2024 3

Secordary index on structured data type.
--->
Secondary index on structured data types.

Here is a mis-spelling error.

from infinity.

yuzhichang avatar yuzhichang commented on May 13, 2024 1

CI improvement: post logs of infinity when CI failure, use Ubuntu 20.04 as base of dev image.
Fuzz test of infinity.

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Kelvinyu1117 avatar Kelvinyu1117 commented on May 13, 2024 1

I would like to contribute to this project, which issue would be a good start?

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JinHai-CN avatar JinHai-CN commented on May 13, 2024
  • Secondary

Fixed and thank you.

from infinity.

yuzhichang avatar yuzhichang commented on May 13, 2024

compatibility testing

image tag refer
centos 7 8 https://hub.docker.com/_/centos/
ubuntu 20.04 22.04 24.04 https://hub.docker.com/_/ubuntu https://releases.ubuntu.com/
debian 8 9 10 11 12 https://hub.docker.com/_/debian https://www.debian.org/releases/
opensuse/leap 15.0 15.1 15.2 15.3 15.4 15.5 https://hub.docker.com/r/opensuse/leap
openeuler/openeuler 20.03   22.03 https://hub.docker.com/r/openeuler/openeuler
openanolis/anolisos 8.6 23 https://hub.docker.com/r/openanolis/anolisos
openkylin/openkylin 1.0 https://hub.docker.com/r/openkylin/openkylin

from infinity.

JinHai-CN avatar JinHai-CN commented on May 13, 2024

@Kelvinyu1117
We do have a couple of issues that might work for contributors new to this project.

  1. Add minmax information to blocks/segments in the current datastore. This information is primarily used for data filtering. (#448)
  2. Implement a bloomfilter for the blocks/segments to enhance point queries. (#467)
  3. Currently, query results are stored in memory in a columnar format. However, the client expects the results in Apache Arrow format. At the moment, the format conversion is executed on the Python client, but this worsens the performance, so we plan to convert the results to Apache Arrow format on the server side before sending them to the client.
  4. There are several optimizer rules to implement, such as constant folding and simplification of arithmetic expressions, which are not yet on the roadmap. Feel free to work on them if interested.
  5. We have additional complicated tasks not listed here. For instance, the current executor operates with one thread per CPU. We're considering using coroutine to enhance efficiency, but we don't have a solid solution yet. If you have experience in this area, you are very welcome to propose your solution.
  6. We understand you're interested in contributing C++ code. However, if that's not the case, there's also unimplemented Python code, such as test cases and the Python SDK API.

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