IMPALA-9110: Add table loading time break-down metrics for HdfsTable

A. Problem:
Catalog table loading currently only records the total loading
time. We will need some break-down times, i.e. more detailed
time recording on each loading function. Also, the table schema
loading is not taken into account for load-duration. We will need
to add some more metrics for that.

B. Solution:
- We added "hms-load-tbl-schema", "load-duration.all-column-stats",
Also, we logged the loadValidWriteIdList() time. So now we have
a more detailed breakdown time for table loading info.

The table loading time metrics for HDFS tables are in the following hierarchy:
- Table Schema Loading
- Table Metadata Loading - total time
    - all column stats loading time
    - ValidWriteIds loading time
    - all partitions loading time - total time
        - file metadata loading time
    - storage-metadata-loading-time(standalone metric)

1. Table Schema Loading:
* Meaning: The time for HMS to fetch table object and the real schema loading time.
Normally, the code path is "msClient.getHiveClient().getTable(dbName, tblName)"
* Metric : hms-load-tbl-schema

2. Table Metadata Loading -- total time
* Meaning: The time to load all the table metadata.
The code path is load() function in HdfsTable.load() function.
* Metric:

2.1 Table Metadata Loading -- all column stats
* Meaning: load all column stats, this is part of table metadata loading
The code path is HdfsTable.loadAllColumnStats()
* Metric: load-duration.all-column-stats

2.2 Table Metadata Loading -- loadValidWriteIdList
* Meaning: fetch ValidWriteIds from HMS
The code path is HdfsTable.loadValidWriteIdList()
* Metric: no metric recorded for this one. Instead, a debug log is

2.3 Table Metadata Loading -- storage metadata loading(standalone metric)
* Meaning: Storage related to file system operations during metadata
loading.(The amount of time spent loading metadata from the underlying storage layer.)
* Metric: we rename it to This is a metric introduced by

2.4 Table Metadata Loading -- load all partitions
* Meaning: Load all partitions time, including fetching all partitions
from HMS and loading all partitions. The code path is
MetaStoreUtil.fetchAllPartitions() and HdfsTable.loadAllPartitions()
* Metric: load-duration.all-partitions

2.4.1 Table Metadata Loading -- load all partitions -- load file metadata
* Meaning: The file metadata loading for all all partitions. (This is
part of 2.4). Code path: loadFileMetadataForPartitions() inside
* Metric: load-duration.all-partitions.file-metadata

C. Extra thing in this commit:
1. Add PrintUtils.printTimeNs for PrettyPrint time in FrontEnd
2. Add explanation for table loading manager

D. Test:
1. Add Unit tests for PrintUtils.printTime() function
2. Manual describe table and verify the table loading metrics are

Change-Id: I5381f9316df588b2004876c6cd9fb7e674085b10
Reviewed-by: Vihang Karajgaonkar <>
Tested-by: Impala Public Jenkins <>
10 files changed
tree: 4ef66c59f99b7f24a6af693f87b6f15e838c1c73
  1. .clang-format
  2. .clang-tidy
  3. .gitattributes
  4. .gitignore
  5. CMakeLists.txt
  7. LICENSE.txt
  9. NOTICE.txt
  11. be/
  12. bin/
  14. cmake_modules/
  15. common/
  16. docker/
  17. docs/
  18. ext-data-source/
  19. fe/
  20. impala-parent/
  21. infra/
  22. lib/
  23. query-event-hook-api/
  24. security/
  25. setup.cfg
  26. shaded-deps/
  27. shell/
  28. ssh_keys/
  29. testdata/
  30. tests/
  31. www/

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 and Amazon S3.
  • Wide analytic SQL support, including window functions and subqueries.
  • On-the-fly code generation using LLVM to generate CPU-efficient code tailored specifically to each individual query.
  • Support for the most commonly-used Hadoop file formats, including the Apache Parquet project.
  • Apache-licensed, 100% open source.

More about Impala

To learn more about Impala as a business user, or to try Impala live or in a VM, please visit the Impala homepage.

If you are interested in contributing to Impala as a developer, or learning more about Impala's internals and architecture, visit the Impala wiki.

Supported Platforms

Impala only supports Linux at the moment.

Export Control Notice

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

Build Instructions

See bin/

Detailed Build Notes

Impala can be built with pre-built components or components downloaded from S3. The components needed to build Impala are Apache Hadoop, Hive, HBase, and Sentry. If you need to manually override the locations or versions of these components, you can do so through the environment variables and scripts listed below.

Scripts and directories
bin/impala-config.shThis script must be sourced to setup all environment variables properly to allow other scripts to work
bin/impala-config-local.shA script can be created in this location to set local overrides for any environment variables
bin/impala-config-branch.shA version of the above that can be checked into a branch for convenience.
bin/bootstrap_build.shA helper script to bootstrap some of the build requirements.
bin/bootstrap_development.shA helper script to bootstrap a developer environment. Please read it before using.
be/build/Impala build output goes here.
be/generated-sources/Thrift and other generated source will be found here.
Build Related Variables
Environment variableDefault valueDescription
IMPALA_HOMETop level Impala directory
IMPALA_TOOLCHAIN“${IMPALA_HOME}/toolchain”Native toolchain directory (for compilers, libraries, etc.)
SKIP_TOOLCHAIN_BOOTSTRAP“false”Skips downloading the toolchain any python dependencies if “true”
CDH_BUILD_NUMBERIdentifier to indicate the CDH build number
CDH_COMPONENTS_HOME“${IMPALA_HOME}/toolchain/cdh_components-${CDH_BUILD_NUMBER}”Location of the CDH components within the toolchain.
CDH_MAJOR_VERSION“5”Identifier used to uniqueify paths for potentially incompatible component builds.
IMPALA_CONFIG_SOURCED“1”Set by ${IMPALA_HOME}/bin/ (internal use)
JAVA_HOME“/usr/lib/jvm/${JAVA_VERSION}”Used to locate Java
JAVA_VERSION“java-7-oracle-amd64”Can override to set a local Java version.
JAVA“${JAVA_HOME}/bin/java”Java binary location.
CLASSPATHSee bin/ for details.
PYTHONPATHWill be changed to include: “${IMPALA_HOME}/shell/gen-py” “${IMPALA_HOME}/testdata” “${THRIFT_HOME}/python/lib/python2.7/site-packages” “${HIVE_HOME}/lib/py” “${IMPALA_HOME}/shell/ext-py/prettytable-0.7.1/dist/prettytable-0.7.1” "${IMPALA_HOME}/shell/ext-py/sasl-0.1.1/dist/sasl-0.1.1-py2.7-linux-x "${IMPALA_HOME}/shell/ext-py/sqlparse-0.1.19/dist/sqlparse-0.1.19-py2
Source Directories for Impala
Environment variableDefault valueDescription
IMPALA_BE_DIR“${IMPALA_HOME}/be”Backend directory. Build output is also stored here.
IMPALA_FE_DIR“${IMPALA_HOME}/fe”Frontend directory
IMPALA_COMMON_DIR“${IMPALA_HOME}/common”Common code (thrift, function registry)
Various Compilation Settings
Environment variableDefault valueDescription
IMPALA_BUILD_THREADS“8” or set to number of processors by default.Used for make -j and distcc -j settings.
IMPALA_MAKE_FLAGS""Any extra settings to pass to make. Also used when copying udfs / udas into HDFS.
USE_SYSTEM_GCC“0”If set to any other value, directs cmake to not set GCC_ROOT, CMAKE_C_COMPILER, CMAKE_CXX_COMPILER, as well as setting TOOLCHAIN_LINK_FLAGS
IMPALA_CXX_COMPILER“default”Used by cmake (cmake_modules/toolchain and clang_toolchain.cmake) to select gcc / clang
USE_GOLD_LINKER“true”Directs backend cmake to use gold.
IS_OSX“false”(Experimental) currently only used to disable Kudu.
Environment variableDefault valueDescription
HADOOP_INCLUDE_DIR“${HADOOP_HOME}/include”For ‘hdfs.h’
HADOOP_LIB_DIR“${HADOOP_HOME}/lib”For ‘libhdfs.a’ or ‘’