Apache Hive integration
editApache Hive integration
edit
Hive is a data warehouse system for Hadoop that facilitates easy data summarization, ad-hoc queries, and the analysis of large datasets stored in Hadoop compatible file systems. |
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-- Hive website |
Hive abstracts Hadoop by abstracting it through SQL-like language, called HiveQL so that users can apply data defining and manipulating operations to it, just like with SQL. In Hive data sets are defined through tables (that expose type information) in which data can be loaded, selected and transformed through built-in operators or custom/user defined functions (or UDFs).
Installation
editMake elasticsearch-hadoop jar available in the Hive classpath. Depending on your options, there are various ways to achieve that. Use ADD command to add files, jars (what we want) or archives to the classpath:
ADD JAR /path/elasticsearch-hadoop.jar;
the command expects a proper URI that can be found either on the local file-system or remotely. Typically it’s best to use a distributed file-system (like HDFS or Amazon S3) and use that since the script might be executed on various machines.
When using JDBC/ODBC drivers, ADD JAR
command is not available and will be ignored. Thus it is recommend to make the jar available to the Hive global classpath and indicated below.
As an alternative, one can use the command-line:
CLI configuration.
$ bin/hive --auxpath=/path/elasticsearch-hadoop.jar
or use the hive.aux.jars.path
property specified either through the command-line or, if available, through the hive-site.xml
file, to register additional jars (that accepts an URI as well):
$ bin/hive -hiveconf hive.aux.jars.path=/path/elasticsearch-hadoop.jar
or if the hive-site.xml
configuration can be modified, one can register additional jars through the hive.aux.jars.path
option (that accepts an URI as well):
hive-site.xml
configuration.
<property> <name>hive.aux.jars.path</name> <value>/path/elasticsearch-hadoop.jar</value> <description>A comma separated list (with no spaces) of the jar files</description> </property>
Configuration
editWhen using Hive, one can use TBLPROPERTIES
to specify the configuration properties (as an alternative to Hadoop Configuration
object) when declaring the external table backed by Elasticsearch:
CREATE EXTERNAL TABLE artists (...) STORED BY 'org.elasticsearch.hadoop.hive.EsStorageHandler' TBLPROPERTIES('es.resource' = 'radio/artists', 'es.index.auto.create' = 'false');
Mapping
editBy default, elasticsearch-hadoop uses the Hive table schema to map the data in Elasticsearch, using both the field names and types in the process. There are cases however when the names in Hive cannot
be used with Elasticsearch (the field name can contain characters accepted by Elasticsearch but not by Hive). For such cases, one can use the es.mapping.names
setting which accepts a comma-separated list of mapped names in the following format: Hive field name
:Elasticsearch field name
To wit:
CREATE EXTERNAL TABLE artists (...) STORED BY 'org.elasticsearch.hadoop.hive.EsStorageHandler' TBLPROPERTIES('es.resource' = 'radio/artists', 'es.mapping.names' = 'date:@timestamp, url:url_123');
Hive column |
Hive is case insensitive while Elasticsearch is not. The loss of information can create invalid queries (as the column in Hive might not match the one in Elasticsearch). To avoid this, elasticsearch-hadoop will always convert Hive column names to lower-case. This being said, it is recommended to use the default Hive style and use upper-case names only for Hive commands and avoid mixed-case names.
Hive treats missing values through a special value NULL
as indicated here. This means that when running an incorrect query (with incorrect or non-existing field names) the Hive tables will be populated with NULL
instead of throwing an exception. Make sure to validate your data and keep a close eye on your schema since updates will otherwise go unnotice due to this lenient behavior.
Writing data to Elasticsearch
editWith elasticsearch-hadoop, Elasticsearch becomes just an external table in which data can be loaded or read from:
CREATE EXTERNAL TABLE artists ( id BIGINT, name STRING, links STRUCT<url:STRING, picture:STRING>) STORED BY 'org.elasticsearch.hadoop.hive.EsStorageHandler' TBLPROPERTIES('es.resource' = 'radio/artists'); -- insert data to Elasticsearch from another table called 'source' INSERT OVERWRITE TABLE artists SELECT NULL, s.name, named_struct('url', s.url, 'picture', s.picture) FROM source s;
Elasticsearch Hive |
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Elasticsearch resource (index and type) associated with the given storage |
For cases where the id (or other metadata fields like ttl
or timestamp
) of the document needs to be specified, one can do so by setting the appropriate mapping, namely es.mapping.id
. Following the previous example, to indicate to Elasticsearch to use the field id
as the document id, update the table
properties:
CREATE EXTERNAL TABLE artists ( id BIGINT, ...) STORED BY 'org.elasticsearch.hadoop.hive.EsStorageHandler' TBLPROPERTIES('es.mapping.id' = 'id'...);
Writing existing JSON to Elasticsearch
editFor cases where the job input data is already in JSON, elasticsearch-hadoop allows direct indexing without applying any transformation; the data is taken as is and sent directly to Elasticsearch. In such cases, one needs to indicate the json input by setting
the es.input.json
parameter. As such, in this case elasticsearch-hadoop expects the output table to contain only one field, who’s content is used as the JSON document. That is, the library will recognize specific textual types (such as string
or binary
) or simply call (toString
).
Table 3. Hive types to use for JSON representation
Hive type |
Comment |
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use this when the JSON data is represented as a |
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use this if the JSON data is represented as a |
anything else |
make sure the |
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use this as an alternative to Hive |
Make sure the data is properly encoded, in UTF-8
. The field content is considered the final form of the document sent to Elasticsearch.
CREATE EXTERNAL TABLE json (data STRING) STORED BY 'org.elasticsearch.hadoop.hive.EsStorageHandler' TBLPROPERTIES('es.resource' = '...', 'es.input.json` = 'yes'); ...
The table declaration only one field of type |
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Indicate elasticsearch-hadoop the table content is in JSON format |
Writing to dynamic/multi-resources
editOne can index the data to a different resource, depending on the row being read, by using patterns. Coming back to the aforementioned media example, one could configure it as follows:
CREATE EXTERNAL TABLE media ( name STRING, type STRING, year STRING, STORED BY 'org.elasticsearch.hadoop.hive.EsStorageHandler' TBLPROPERTIES('es.resource' = 'my-collection-{type}/doc');
Table field used by the resource pattern. Any of the declared fields can be used. |
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Resource pattern using field |
For each row about to be written, elasticsearch-hadoop will extract the type
field and use its value to determine the target resource.
The functionality is also available when dealing with raw JSON - in this case, the value will be extracted from the JSON document itself. Assuming the JSON source contains documents with the following structure:
the table declaration can be as follows:
CREATE EXTERNAL TABLE json (data STRING) STORED BY 'org.elasticsearch.hadoop.hive.EsStorageHandler' TBLPROPERTIES('es.resource' = 'my-collection-{media_type}/doc', 'es.input.json` = 'yes');
Schema declaration for the table. Since JSON input is used, the schema is simply a holder to the raw data |
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Resource pattern relying on fields within the JSON document and not on the table schema |
Reading data from Elasticsearch
editReading from Elasticsearch is strikingly similar:
CREATE EXTERNAL TABLE artists ( id BIGINT, name STRING, links STRUCT<url:STRING, picture:STRING>) STORED BY 'org.elasticsearch.hadoop.hive.EsStorageHandler' TBLPROPERTIES('es.resource' = 'radio/artists', 'es.query' = '?q=me*'); -- stream data from Elasticsearch SELECT * FROM artists;
Type conversion
editIf automatic index creation is used, please review this section for more information.
Hive provides various types for defining data and internally uses different implementations depending on the target environment (from JDK native types to binary-optimized ones). Elasticsearch integrates with all of them, including and Serde2 lazy and lazy binary:
Hive type | Elasticsearch type |
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not supported (yet) |
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While Elasticsearch understands Hive types up to version 2.0, it is backwards compatible with Hive 1.0
It is worth mentioning that rich data types available only in Elasticsearch, such as GeoPoint
or GeoShape
are supported by converting their structure into the primitives available in the table above. For example, based on its storage a geo_point
might be
returned as a string
or an array
.