Developer Guide

Code Organization and a

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Brief Architecture

Introduction

Hive has 3 main components:

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• Command Line Interface (trunk/cli) - This component has all the java code used by the Hive command line interface.
• Hive Server (trunk/service) - This component implements all the APIs that can be used by other clients (such as JDBC drivers) to talk to Hive.
• Common (trunk/common) - This component contains common infrastructure needed by the rest of the code. Currently, this contains all the java sources for managing and passing Hive configurations(HiveConf) to all the other code components.
• Ant Utilities (trunk/ant) - This component contains the implementation of some ant tasks that are used by the build infrastructure.
• Scripts (trunk/bin) - This component contains all the scripts provided in the distribution including the scripts to run the Hive cliHive CLI (bin/hive).

The following top level directories contain helper libraries, packaged configuration files etc..:

• trunk/conf - This directory contains the packaged hive-default.xml and hive-site.xml.
• trunk/data - This directory contains some data sets and configurations used in the hive Hive tests.
• trunk/ivy - This directory contains the ivy Ivy files used by the build infrastructure to manage dependencies on different hadoop Hadoop versions.
• trunk/lib - This directory contains the run time libraries needed by Hive.
• trunk/testlibs - This directory contains the junit.jar used by the junit target in the build infrastructure.
• trunk/testutils (Deprecated)

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• SerDe is a short name for "Serializer and Deserializer."
• Hive uses SerDe (and ! FileFormat) to read and write table rows.
• HDFS files --> InputFileFormat --> <key, value> --> Deserializer --> Row object
• Row object --> Serializer --> <key, value> --> OutputFileFormat --> HDFS files

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Note that org.apache.hadoop.hive.serde is the deprecated old serde SerDe library. Please look at org.apache.hadoop.hive.serde2 for the latest version.

Hive currently uses these FileFormat classes to read and write HDFS files:

• !TextInputFormat/HiveIgnoreKeyTextOutputFormat: These 2 classes read/write data in plain text file format.
• !SequenceFileInputFormat/SequenceFileOutputFormat: These 2 classes read/write data in hadoop !Hadoop SequenceFile format.

Hive currently uses these ! SerDe classes to serialize and deserialize data:

• !MetadataTypedColumnsetSerDe: This ! SerDe is used to read/write delimited records like CSV, tab-separated control-A separated records (sorry, quote is not supported yet.)
• !ThriftSerDe: This ! SerDe is used to read/write thrift Thrift serialized objects. The class file for the Thrift object must be loaded first.
• !DynamicSerDe: This ! SerDe also read/write thrift Thrift serialized objects, but it understands thrift Thrift DDL so the schema of the object can be provided at runtime. Also it supports a lot of different protocols, including ! TBinaryProtocol, ! TJSONProtocol, TCTL!SeparatedProtocol TCTLSeparatedProtocol (which writes data in delimited records).

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See SerDe for detailed information about input and output processing. Also see Storage Formats in the HCatalog manual, including CTAS Issue with JSON SerDe. For information about how to create a table with a custom or native SerDe, see Row Format, Storage Format, and SerDe.

How to write your own ! SerDe:

• In most cases, users want to write a Deserializer instead of a ! SerDe, because users just want to read their own data format instead of writing to it.
• For example, the ! RegexDeserializer will deserialize the data using the configuration parameter 'regex', and possibly a list of column names (see serde2.MetadataTypedColumnsetSerDe). Please see serde2/Deserializer.java for details.
• If your ! SerDe supports DDL (basically, ! SerDe with parameterized columns and column types), you probably want to implement a Protocol based on ! DynamicSerDe, instead of writing a ! SerDe from scratch. The reason is that the framework passes DDL to ! SerDe through "thrift Thrift DDL" format, and it's non-trivial to write a "thrift Thrift DDL" parser.
• For examples, see SerDe - how to add a new SerDe below.

Some important points about ! SerDe:

• !SerDe, not the DDL, defines the table schema. Some ! SerDe implementations use the DDL for configuration, but the ! SerDe can also override that.
• Column types can be arbitrarily nested arrays, maps, and structures.
• The callback design of ! ObjectInspector allows lazy deserialization with CASE/IF or when using complex or nested types.

ObjectInspector

Hive uses ! ObjectInspector to analyze the internal structure of the row object and also the structure of the individual columns.

!ObjectInspector provides a uniform way to access complex objects that can be stored in multiple formats in the memory, including:

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A complex object can be represented by a pair of ! ObjectInspector and Java Object. The ! ObjectInspector not only tells us the structure of the Object, but also gives us ways to access the internal fields inside the Object.

NOTE: Apache Hive recommends that custom ObjectInspectors created for use with custom Serdes SerDes have a no-argument constructor in addition to their normal constructors for serialization purposes. See HIVE-5380 for more details.

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• Metastore Server - This is the thrift Thrift server (interface defined in metastore/if/hive_metastore.if) that services metadata requests from clients. It delegates most of the requests underlying meta data store and the Hadoop file system which contains data.
• Object Store - ObjectStore class handles access to the actual metadata is stored in the SQL store. The current implementation uses JPOX ORM solution which is based of JDA specification. It can be used with any database that is supported by JPOX. New meta stores (file based or xml based) can added by implementing the interface MetaStore. FileStore is a partial implementation of an older version of metastore which may be deprecated soon.
• Metastore Client - There are python, java, php thrift Thrift clients in metastore/src. Java generated client is extended with HiveMetaStoreClient which is used by Query Processor (ql/metadta). This is the main interface to all other Hive components.

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will make Hive compile against hadoop Hadoop version 0.19.0. Note that:

• Hive uses Ivy to download the hadoop-0.19.0 distribution. However once downloaded, it's cached and not downloaded multiple times.
• This will create a distribution directory in build/dist (relative to the source root) from where one can launch Hive. This distribution should only be used to execute queries against hadoop Hadoop branch 0.19. (Hive is not sensitive to minor revisions of Hadoop versions).

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• One can specify a version of hadoop Hadoop other than 0.19.0 by using (using 0.17.1 as an example):

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• One can also compile against a custom version of the Hadoop tree (only release 0.4 and above). This is also useful if running Ivy is problematic (in disconnected mode for example) - but a hadoop Hadoop tree is available. This can be done by specifying the root of the hadoop Hadoop source tree to be used, for example:

ant -Dhadoop.root=~/src/hadoop-19/build/hadoop-0.19.2-dev -Dhadoop.version=0.19.2-dev

note that:

• hiveHive's build script assumes that hadoop.root is pointing to a distribution tree for hadoop Hadoop created by running ant package in hadoopHadoop.
• hadoop.version must match the version used in building hadoopHadoop.

In this particular example - ~/src/hadoop-19 is a checkout of the hadoop Hadoop 19 branch that uses 0.19.2-dev as default version and creates a distribution directory in build/hadoop-0.19.2-dev by default.

Run hive Hive from the command line with '$HIVE_HOME/bin/hive', where $HIVE_HOME is typically build/dist under your hive Hive repository top-level directory.

$ build/dist/bin/hive

If hive Hive fails at runtime, try 'ant very-clean package' to delete the ivy Ivy cache before rebuilding.

Running Hive Without a Hadoop Cluster

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Hive uses junit for unit tests. Each of the 3 main components of Hive have their unit test implementations in the corresponding src/test directory e.g. trunk/metastore/src/test has all the unit tests for metastore, trunk/serde/src/test has all the unit tests for serde and trunk/ql/src/test has all the unit tests for the query processor. The metastore and serde unit tests provide the ! TestCase implementations for junit. The query processor tests on the other hand are generated using Velocity. The main directories under trunk/ql/src/test that contain these tests and the corresponding results are as follows:

• Test Queries:
  • queries/clientnegative - This directory contains the query files (.q files) for the negative test cases. These are run through the CLI classes and therefore test the entire query processor stack.
  • queries/clientpositive - This directory contains the query files (.q files) for the positive test cases. Thesre are run through the CLI classes and therefore test the entire query processor stack.
  • qureies/positive (Will be deprecated) - This directory contains the query files (.q files) for the positive test cases for the compiler. These only test the compiler and do not run the execution code.
  • queries/negative (Will be deprecated) - This directory contains the query files (.q files) for the negative test cases for the compiler. These only test the compiler and do not run the execution code.
• Test Results:
  • results/clientnegative - The expected results from the queries in queries/clientnegative.
  • results/clientpositive - The expected results from the queries in queries/clientpositive.
  • results/compiler/errors - The expected results from the queries in queries/negative.
  • results/compiler/parse - The expected Abstract Syntax Tree output for the queries in queries/positive.
  • results/compiler/plan - The expected query plans for the queries in queries/positive.
• Velocity Templates to Generate the tests:
  • templates/!TestCliDriver.vm - Generates the tests from queries/clientpositive.
  • templates/!TestNegativeCliDriver.vm - Generates the tests from queries/clientnegative.
  • templates/!TestParse.vm - Generates the tests from queries/positive.
  • templates/!TestParseNegative.vm - Generates the tests from queries/negative.

    Tables in the unit tests

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Note that this option matches against the basename of the test without the .q suffix.

Apparently the hive Hive tests do not run successfully after a clean unless you run ant package first. Not sure why build.xml doesn't encode this dependency.

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First, write a new myname.q in ql/src/test/queries/clientpositive.

Then, run the test with the query and overwrite the result (useful when you add a new test).

ant test -Dtestcase=TestCliDriver -Dqfile=myname.q -Doverwrite=true

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Similarly, to add negative client tests, write a new query input file in ql/src/test/queries/clientnegative and run the same command, this time specifying the testcase name as ! TestNegativeCliDriver instead of ! TestCliDriver. Note that for negative client tests, the output file if created using the overwrite flag can be be found in the directory ql/src/test/results/clientnegative.

See also Tips for adding new Tests.

Debugging Hive Code

Debugging Hive code

• make Make sure that you have run ant model-jar in hive/metastore and ant gen-test in hive since the last time you ran ant clean.
• To run all of the unit tests for the Cli, open up TestCliDriver.java
  • click Run->Debug Configurations , select TestCliDriver, and click Debug
• To run a single test within TestCliDriver.java
  • Begin running the whole TestCli suite as before
  • Once it finishes the setup and starts executing the JUnit tests, stop the test execution
  • Find the desired test in the JUnit pane
  • Right click on that test and select Debug

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• Compile Hive code with javac.debug=on. Under Hive checkout directory.:

  Code Block
  > ant -Djavac.debug=on package

  If you have already built Hive without javac.debug=on, you can clean the build and then run the above command.

  Code Block
  > ant clean # not necessary if the first time to compile > ant -Djavac.debug=on package

• Run ant test with additional options to tell the Java VM that is running Hive server-side code to wait for the debugger to attach. First define some convenient macros for debugging. You can put it in your .bashrc or .cshrc.

  Code Block
  > export HIVE_DEBUG_PORT=8000 > export HIVE_DEBUG="-Xdebug -Xrunjdwp:transport=dt_socket,address=${HIVE_DEBUG_PORT},server=y,suspend=y"

  In particular HIVE_DEBUG_PORT is the port number that the JVM is listening on and the debugger will attach to. Then run the unit test as follows:

  Code Block
  > export HADOOP_OPTS=$HIVE_DEBUG > ant test -Dtestcase=TestCliDriver -Dqfile=<mytest>.q

  The unit test will run until it shows:

  Code Block
  [junit] Listening for transport dt_socket at address: 8000

• Now, you can use jdb to attach to port 8000 to debug

  Code Block
  > jdb -attach 8000

  or if you are running Eclipse and the Hive projects are already imported, you can debug with Eclipse. Under Eclipse Run -> Debug Configurations, find "Remote Java Application" at the bottom of the left panel. There should be a MapRedTask configuration already. If there is no such configuration, you can create one with the following property:

• Name: any task such as MapRedTask
• Project: the Hive project that you imported.
• Connection Type: Standard (Socket Attach)
• Connection Properties:
  • Host: localhost
  • Port: 8000
    Then hit the "Debug" button and Eclipse will attach to the JVM listening on port 8000 and continue running till the end. If you define breakpoints in the source code before hitting the "Debug" button, it will stop there. The rest is the same as debugging client-side Hive.

There is another way of debugging hive Hive code without going through antAnt.
You need to install hadoop Hadoop and set the environment variable HADOOP_HOME to that.

    > export HADOOP_HOME=<your hadoop home>
 

Then, start hiveHive:

    >  ./build/dist/bin/hive --debug
 

It will then act similar to the debugging steps outlines in Debugging Hive code. It is faster since there is no need to compile hive Hive code,
and go through antAnt. It can be used to debug both client side and server side hiveHive. If you want to debug a particular query, start hiveHive
and perform the stops needed before that query. Then start, hive start Hive again in debug to debug that query.

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Note that the local file system will be used, so the space no your machine will not be released automatically (unlike debugging via antAnt, where the tables created in test are automatically dropped at the end of the test). Make sure to either drop the tables explicitly, or drop the data from /User/hive/warehouse.

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