A hive adapter for data stored in Kiji tables
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The official Hive adapter for Kiji Schema 1.3.x.

The Kiji Hive Adapter can be used to for access to data stored in Kiji tables from Hive. For write access do be aware of some of the caveats specified in the Write Access Caveats section below.


  • Hadoop 2.0.0-mr1-cdh4.3.0
  • HBase 0.94.6-cdh4.3.0
  • Hive 0.10.0-cdh4.3.0
  • KijiSchema 1.3.x

Automatic Hive Shell

The included bin/bento-hive.sh script can be executed to automatically start a Hive shell with the Kiji Hive adapter(and its dependencies preloaded). Any jars that are referenced in HADOOP_CLASSPATH will also be added within the Hive shell. If necessary, this script will automatically download Apache Hive from Cloudera.

This script can take an argument of a Kiji table URI to automatically create the table handler from the specified table. Currently this only supports String types, but the generated SQL can be modified to change the relevant types.

Manual Hive Installation

If you don't already have Apache Hive installed, you can download it from Cloudera:

$ wget http://archive.cloudera.com/cdh4/cdh/4/hive-0.10.0-cdh4.3.0.tar.gz

Extract the archive

$ tar -xzf hive-0.10.0-cdh4.3.0.tar.gz

Set up your environment by setting the HIVE_HOME environment variable and adding the hive commands to your path:

$ export HIVE_HOME=/path/to/hive-0.10.0-cdh4.3.0
$ export PATH=$PATH:$HIVE_HOME/bin

If you are seeing NoClassDefFoundErrors for an Avro class, likely the issue is a result of a classpath ordering issue on your cluster (many Hadoop distributions include a version of Avro which may be incompatible with the one included as part of the Kiji Hive Adapter). To get around this, the configuration property 'mapreduce.task.classpath.user.precedence' must be set to true. This can be done either as part of the Hive configuration (see the source for the bento-hive.sh script for an example) or as part of the cluster configuration (by modifying the appropriate site.xml configuration).

Manual Setup

To use the Kiji Hive Adapter, start hive with the JAR on your classpath with the HADOOP_CLASSPATH environment variable.

$ HADOOP_CLASSPATH=/path/to/kiji-hive-adapter-${project.version}.jar

At the hive shell, add required JARs to the distributed cache using the 'add jar' command. Add the Kiji Hive Adapter JAR and the HBase JAR from $HBASE_HOME.

hive> add jar /path/to/kiji-hive-adapter-${project.version}.jar;
hive> add jar /path/to/hbase-<version>.jar;

You will need to add these JARs each time you start a hive session.

Creating Views of Kiji Tables

There are 4 things you will always specify when creating Hive table view over an existing Kiji table.

  1. Use the EXTERNAL keyword to reference an existing Kiji table.
  2. Use STORED BY 'org.kiji.hive.KijiTableStorageHandler' so Hive knows how to speak Kiji.
  3. Use WITH SERDEPROPERTIES to specify the mapping of Kiji table columns into Hive columns using the 'kiji.columns' property.
  4. (Optional) Use the 'kiji.entity.id.shell.string' within the WITH SERDEPROPERTIES section to specity the source of the EntityId shell string to create a view that is writable.
  5. Use TBLPROPERTIES to specify the URI of the Kiji table.

This is best explained with an example.

Sample Table

The Kiji Music example has an example table with some imported data that is imported as part of the tutorial. The table that we will be using is a users table that tracks each individual user, their historical playlist, and their next song recommendations. The SQL statement necessary to setup this table to be used in Hive looks like:

    user STRING,
    track_plays STRUCT<ts: TIMESTAMP, value: STRING>,
    next_song_rec STRUCT<ts: TIMESTAMP, value: STRING>
STORED BY 'org.kiji.hive.KijiTableStorageHandler'
    'kiji.columns' = ':entity_id,info:track_plays[0],info:next_song_rec[0]',
    'kiji.entity.id.shell.string' = 'user'
    'kiji.table.uri' = 'kiji://.env/kiji_music/users'

If you'd like to load this automatically you can run the script:

bin/bento-hive.sh import kiji://.env/kiji_music/users

against a Bento cluster where this table has already been created and has the data populated. This functionality is also available in the kiji CLI tool via the command:

kiji generate-hive-table --kiji=kiji://.env/kiji_music/users

Cell paging

Occasionally a column will be too large to fit into memory, and so the only recourse is to page through the column for results. To do this, add the entries corresponding to your desired column and pagesize(in entries) to the SERDEPROPERIES section of the CREATE_EXTERNAL_TABLE statement:

'kiji.cell.paging.info:track_plays' = '10'

Note that you'll likely need to comma delimit the entries, so if you wanted to page through both the info:track_plays column and the info:next_song_rec column, it'd look like the following:

'kiji.cell.paging.info:track_plays' = '10',
'kiji.cell.paging.info:next_song_rec' = '5'

Qualifier paging

When using map type column families, sometimes there will be too many qualifiers to fit into memory. To page through these qualifiers, add the entries corresponding to your desired column family and page size(in number of qualifiers) to the SERDEPROPERTIES of the CREATE EXTERNAL TABLE statement:

'kiji.qualifier.paging.infomap' = '5'

Note that you can add cell level paging to all qualifiers of this map type family via:

'kiji.qualifier.paging.infomap' = '5',
'kiji.cell.paging.infomap' = '10'

However, configuring both the cell level paging for a family and a fully qualified column simultaneously will result in an error.

Sample Queries

These SQL statements all rely on the above table being created in Hive. You can start a Hive shell with the necessary jars for Kiji tables via the command: bin/bento-hive.sh shell

List all of the tables that have been created:


List all of the fields in the table 'users':


List all of the tracks that were played:

SELECT track_plays.value FROM users;

Show only the first 10 users:

SELECT track_plays.value FROM users LIMIT 10;

List all tracks that were played by play order:

SELECT track_plays.ts,track_plays.value FROM users order by ts ASC;

List all of the times in which song-44 was played:

SELECT track_plays.ts,track_plays.value FROM users WHERE track_plays.value = 'song-44';

List all songs that were played before the hour is less than 10 AM:

SELECT track_plays.ts, track_plays.value FROM users WHERE HOUR(track_plays.ts) < 10;

Get a list of songs by popularity:

SELECT track_plays.value,COUNT(1) AS COUNT FROM users group by track_plays.value ORDER BY COUNT DESC;

If you've created this users in a different instance as new_users, you can use the write functionality to copy rows between these:


Or alternately if you want to save the intermediate results of computations that are done in Hive, you can create a temporary table with:

CREATE TABLE temporary_results AS <YOUR QUERY>

This table can then be queried/joined as part of future queries.

If you want more inspiration for queries take a look here: https://cwiki.apache.org/Hive/tutorial.html#Tutorial-UsageandExamples

Exporting results to CSV

In many cases, you’ll want to use results of a Hive query in another tool that’s more conducive for analysis. Depending on the size of the results, CSVs are often the best choice for this. The first command ensures that the output isn’t compressed, and then you can prepend the CSV definition to your query as follows:

set hive.exec.compress.output=false;

These commands won’t actually overwrite any such data that you’ve already written out, if you get an error message like: “Table already exists: outputcsv”, you’ll have to either select a new destination, or remove the existing table by dropping it:

DROP TABLE outputcsv;

Now to copy this over to your local machine, you can use hadoop’s getmerge functionality(which will combine potentially numerous files into a single result as well):

!hadoop fs -getmerge /user/hive/warehouse/outputcsv/ output.csv;

Using data in other tools

Importing data into R

Now that the data exists in a CSV format, loading it into R is the same as for any other CSV:

mydata <- read.table("output.csv", sep=",")

Importing data into Kiji Express

If the data is on your local machine, processing can be done within the Kiji Express as a CSV input using the standard split methods:

val input = TextLine("output.csv")
val userSongs = input.map('line -> ('user, 'song)) { 
    line: String => (line.split(",")(0), 

Mapping Kiji Table Data into Hive

Each column declared in the Hive table needs to have a corresponding entry in the 'kiji.columns' property of the WITH SERDEPROPERTIES clause. The value of 'kiji.columns' is a comma-separated list of Kiji row expressions. The number of Hive columns declared in the CREATE EXTERNAL TABLE clause must match the number of Kiji row expressions in your 'kiji.columns' value.

Kiji Row Expressions

A Kiji row expression addresses a piece of data in a Kiji table row. The form of a Kiji row expression is:

| Expression          | Hive Type                                   |
| family              | MAP<STRING, ARRAY<STRUCT<TIMESTAMP, cell>>> |
| family[n]           | MAP<STRING, STRUCT<TIMESTAMP, cell>>        |
| family:qualifier    | ARRAY<STRUCT<TIMESTAMP, cell>>              |
| family:qualifier[n] | STRUCT<TIMESTAMP, cell>                     |

It is important that the types of the Hive columns in the CREATE statement match the type of data generated by the Kiji row expressions according to the table above. For a family expression, the column will contain a map from qualifier to the array of cells in reverse chronological order. For column expressions, the column will contain a struct with two fields: the timestamp and the cell value.

The symbol n specifies the index of the cell to retrieve, where the 0-th cell is the one with the greatest timestamp (most recent). For example, event:click[0] represents the most recent cell from the click column of the event family, and event:click[1] represents the second most recent cell (the previous click). You may also use the special index -1 to specify the cell with the least timestamp (oldest).

The type of the cell value depends on the Avro data type in the Kiji table. For primitive types, the relationship is obvious (e.g. an Avro "string" maps to a Hive STRING).

| Avro Type                  | Hive Type                 |
| "boolean"                  | BOOLEAN                   |
| "int"                      | INT                       |
| "long"                     | BIGINT, TIMESTAMP         |
| "float"                    | FLOAT                     |
| "double"                   | DOUBLE                    |
| "string"                   | STRING                    |
| "bytes"                    | BINARY                    |
| "fixed"                    | BINARY, BYTE, SHORT       |
| "null"                     | VOID                      |
| record { T f1; U f2; ... } | STRUCT<f1: T, f2: U>      |
| map<T>                     | MAP<STRING, T>            |
| array<T>                   | ARRAY<T>                  |
| union {T, U, ...}          | UNIONTYPE<T, Y, ...>      |
| union {null, T}            | T                         |

Note that we special case union types with null to just return the raw Hive type, since Hive types are already nullable.

For any types that aren't explicitly supported within Hive(like enumerations), the STRING type can be used in the Hive definition to use the type's toString() representation.

There are two additional meta columns that are available to allow you to read the entity ID for a row.

| Expression          | Hive Type                                   |
| :entity_id          | STRING                                      |
| :entity_id[n]       | INT, BIGINT, or STRING                      |

The first form requires the literal string :entity_id and will result in a STRING formatted entity ID. The second form allows you to specify a component index and will result in the value of that component, which can be one of INT, BIGINT, or STRING.

Write access caveats

In order for the Kiji Hive Adapter to write back to a Kiji table, we need to determine the row key to write back to. Currently we use the notion of the EntityId's shell string(found in the Kiji CLI) to form the row key. There is a ticket(KIJIHIVE-30) to support building the EntityId from columns representing each of the row key components.

For writing to a writable Hive view of a table, it is required that all columns are present within the query. This is a limitation of Hive, and if you only only are writing a subset of columns, you will need to create another view that only contains the relevant columns(and the EntityId).

Kiji Hive Adapter currently only supports write access with primitive types. Complex Avro records would involve work done on KIJIHIVE-31. In addition, to determine the row key to write to(the Kiji EntityId), we require the use of the EntityId's shell string(which is the view presented within the Kiji CLI.

Happy querying!