IMPALA-11740: Incorrect results for partitioned Iceberg V2 tables when runtime filters are applied

If an Iceberg V2 table is partitioned, and contains delete files,
then in a query that involves runtime filters on the partition columns
return empty result set.

E.g.:

  select count(*)
  from store_sales, date_dim
  where d_date_sk = ss_sold_date_sk and d_moy=2 and d_year=1998;

In the above query store_sales is partitioned by ss_sold_date_sk which
will be filtered by runtime filters created by the JOIN. If store_sales
has delete files then the above query returns empty result set.

The problem is that we are invoking PartitionPassesFilters() on these
Iceberg tables. It is usually a no-op for Iceberg tables, as the
template tuple is NULL. But when we have virtual columns a template
tuple has been created in HdfsScanPlanNode::InitTemplateTuple. For
Iceberg tables this tempalte tuple is incomplete, i.e. it doesn't
have the partition values set. This means the filters evaluate to
false and the files are getting filtered out, hence the query
produces an empty result set.

With this patch we don't invoke PartitionPassesFilters() on Iceberg
tables, only the Iceberg-specific IcebergPartitionPassesFilters()
gets invoked. Also added DCHECKs to ensure this.

Testing:
 * e2e tests added

Change-Id: I43f3e0a4df7c1ba6d8ea61410b570d8cf7b31ad3
Reviewed-on: http://gerrit.cloudera.org:8080/19274
Reviewed-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>
Tested-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>
5 files changed
tree: d92c30c409779adddcde0d5e0d3fed4d68d32980
  1. be/
  2. bin/
  3. cmake_modules/
  4. common/
  5. docker/
  6. docs/
  7. fe/
  8. infra/
  9. java/
  10. lib/
  11. security/
  12. shell/
  13. ssh_keys/
  14. testdata/
  15. tests/
  16. www/
  17. .clang-format
  18. .clang-tidy
  19. .gitattributes
  20. .gitignore
  21. buildall.sh
  22. CMakeLists.txt
  23. EXPORT_CONTROL.md
  24. LICENSE.txt
  25. LOGS.md
  26. NOTICE.txt
  27. README-build.md
  28. README.md
  29. setup.cfg
README.md

Welcome to Impala

Lightning-fast, distributed SQL queries for petabytes of data stored in Apache Hadoop clusters.

Impala is a modern, massively-distributed, massively-parallel, C++ query engine that lets you analyze, transform and combine data from a variety of data sources:

  • Best of breed performance and scalability.
  • Support for data stored in HDFS, Apache HBase, Apache Kudu, Amazon S3, Azure Data Lake Storage, Apache Hadoop Ozone and more!
  • Wide analytic SQL support, including window functions and subqueries.
  • On-the-fly code generation using LLVM to generate lightning-fast code tailored specifically to each individual query.
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More about Impala

The fastest way to try out Impala is a quickstart Docker container. You can try out running queries and processing data sets in Impala on a single machine without installing dependencies. It can automatically load test data sets into Apache Kudu and Apache Parquet formats and you can start playing around with Apache Impala SQL within minutes.

To learn more about Impala as a user or administrator, or to try Impala, please visit the Impala homepage. Detailed documentation for administrators and users is available at Apache Impala documentation.

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Supported Platforms

Impala only supports Linux at the moment. Impala supports x86_64 and has experimental support for arm64 (as of Impala 4.0). Impala Requirements contains more detailed information on the minimum CPU requirements.

Supported OS Distributions

Impala runs on Linux systems only. The supported distros are

  • Ubuntu 16.04/18.04
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Other systems, e.g. SLES12, may also be supported but are not tested by the community.

Export Control Notice

This distribution uses cryptographic software and may be subject to export controls. Please refer to EXPORT_CONTROL.md for more information.

Build Instructions

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Detailed build notes has some detailed information on the project layout and build.