This document discusses integrating Apache Hive with HBase. It describes how Hive can be used to query HBase tables via a storage handler. Key features covered include using HBase as a data source or sink for Hive, mapping Hive schemas and types to HBase schemas, pushing filters down to HBase, and bulk loading data. The future of Hive and HBase integration could include improvements to schema mapping, filter pushdown support, and leveraging new HBase typing APIs.

1. © Hortonworks Inc. 2011
Integration of Apache Hive
and HBase
Enis Soztutar
enis [at] apache [dot] org
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Ashutosh Chauhan
hashutosh [at] apache [dot] org

2. © Hortonworks Inc. 2011
About Us
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Architecting the Future of Big Data
Enis Soztutar
• In the Hadoop space since 2007
• Committer and PMC Member in Apache HBase and Hadoop
• Twitter: @enissoz
Ashutosh Chauhan
• In the Hadoop space since 2009
• Committer and PMC Member in Apache Hive and Pig

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Agenda
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Architecting the Future of Big Data
• Overview of Hive
• Hive + HBase Features and Improvements
• Future of Hive and HBase
• Q&A

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Apache Hive Overview
• Apache Hive is a data warehouse system for Hadoop
• SQL-like query language called HiveQL
• Built for PB scale data
• Main purpose is analysis and ad hoc querying
• Database / table / partition / bucket – DDL Operations
• SQL Types + Complex Types (ARRAY, MAP, etc)
• Very extensible
• Not for : small data sets, OLTP
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Apache Hive Architecture
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Architecting the Future of Big Data
Metastore
RDBMS
Hive Thrift
Server
Driver
CLI
JDBC/ODBC
Hive Web
Interface
HDFS
MapReduce
Execution
Parser Planner
Optimizer
M
S
C
l
i
e
n
t

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Hive + HBase Features and
Improvements
Architecting the Future of Big Data
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Hive + HBase Motivation
• Hive over HDFS and HBase has different characteristics
– Batch Online
– Structured vs Unstructured
– Analysts Programmers
• Hive datawarehouses on HDFS are
– Long ETL times
– Access to real time data
• Analyzing HBase data with MapReduce requires
custom coding
• Hive and SQL are already known by many analysts
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Use Case 1: HBase as ETL Data Sink
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From HUG - Hive/HBase Integration or, MaybeSQL? April 2010 John Sichi Facebook
http://www.slideshare.net/hadoopusergroup/hive-h-basehadoopapr2010
HDFS
Tables
INSERT …
SELECT …!
FROM … !
HBase
Online Queries

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Use Case 2: HBase as Data Source
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From HUG - Hive/HBase Integration or, MaybeSQL? April 2010 John Sichi Facebook
http://www.slideshare.net/hadoopusergroup/hive-h-basehadoopapr2010
HDFS
Tables
SELECT …
JOIN …!
GROUP BY … !
HBase
Query
Result

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Use Case 3: Low Latency Warehouse
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From HUG - Hive/HBase Integration or, MaybeSQL? April 2010 John Sichi Facebook
http://www.slideshare.net/hadoopusergroup/hive-h-basehadoopapr2010
HDFS
Tables
HBase
Continuous
Updates
HIVE QUERIES!
Periodic
Dump

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Hive + HBase Example (HBase table)
hbase(main):001:0> create 'short_urls', {NAME => 'u'},
{NAME=>'s'}
hbase(main):014:0> scan 'short_urls'
ROW COLUMN+CELL
bit.ly/aaaa column=s:hits, value=100
bit.ly/aaaa column=u:url, value=hbase.apache.org/
bit.ly/abcd column=s:hits, value=123
bit.ly/abcd column=u:url, value=example.com/foo
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Hive + HBase Example (Hive table)
CREATE TABLE short_urls(
short_url string,
url string,
hit_count int
)
STORED BY
'org.apache.hadoop.hive.hbase.HBaseStorageHandler'
WITH SERDEPROPERTIES
("hbase.columns.mapping" = ":key, u:url, s:hits")
TBLPROPERTIES
("hbase.table.name" = ”short_urls");
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Storage Handler
• Hive defines HiveStorageHandler class for different storage
backends: HBase/ Cassandra / MongoDB/ etc
• Storage Handler has hooks for
– Getting input / output formats
– Meta data operations hook: CREATE TABLE, DROP TABLE,
etc
• Storage Handler is a table level concept
– Does not support Hive partitions, and buckets
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Apache Hive + HBase Architecture
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Architecting the Future of Big Data
Metastore
RDBMS
Hive Thrift
Server
Driver
CLI
Hive Web
Interface
HDFS
MapReduce
Execution
Parser Planner
Optimizer
M
S
C
l
i
e
n
t
HBase
StorageHandler

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Hive + HBase Integration
• For Input/OutputFormat, getSplits(), etc underlying HBase
classes are used
• Column selection and certain filters can be pushed down
• HBase tables can be used with other(Hadoop native) tables
and SQL constructs
• Hive DDL operations are converted to HBase DDL
operations via the client hook.
– All operations are performed by the client
– No two phase commit
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Schema / Type Mapping
Architecting the Future of Big Data
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Schema Mapping
• Hive table + columns + column types <=> HBase table + column
families (+ column qualifiers)
• Every field in Hive table is mapped to either
– The table key (using :key as selector)
– A column family (cf:) -> MAP fields in Hive
– A column (cf:cq)
• Hive table does not need to include all columns in Hbase
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Schema Mapping - Example
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CREATE TABLE short_urls(
short_url string,
url string,
hit_count int,
props, map<string,string>
)
WITH SERDEPROPERTIES
("hbase.columns.mapping" = ":key, u:url, s:hits, p:")

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Schema Mapping - Example
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Architecting the Future of Big Data
CREATE TABLE short_urls(
short_url string,
url string,
hit_count int,
props map<string,string>
)
WITH SERDEPROPERTIES
("hbase.columns.mapping" = ":key, u:url, s:hits, p:")

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Type Mapping
• Added in Hive (0.9.0)
• Previously all types were being converted to strings in HBase
• Hive has:
– Primitive types: INT, STRING, BINARY, DOUBLE etc
– ARRAY<Type>
– MAP<PrimitiveType, Type>
– STRUCT<a:INT, b:STRING, c:STRING>
• HBase does not have types
– Bytes.toBytes()
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Type Mapping
• Table level property
"hbase.table.default.storage.type” = “binary”
• Type mapping can be given per column after #
– Any prefix of “binary” , eg u:url#b
– Any prefix of “string” , eg u:url#s
– The dash char “-” , eg u:url#-
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Type Mapping - Example
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CREATE TABLE short_urls(
short_url string,
url string,
hit_count int,
props, map<string,string>
)
WITH SERDEPROPERTIES
("hbase.columns.mapping" = ":key#b,u:url#b,s:hits#b,p:#s")

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Type Mapping
• If the type is not a primitive or Map, it is converted to a JSON
string and serialized
• Still a few rough edges for schema and type mapping:
– No support for DECIMAL, BINARY Hive types
– No mapping of HBase timestamp (can only provide put
timestamp)
– No arbitrary mapping of Structs / Arrays into HBase schema
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Bulk Load
• Steps to bulk load:
– Sample source data for range partitioning
– Save sampling results to a file
– Run CLUSTER BY query using HiveHFileOutputFormat and
TotalOrderPartitioner
– Import Hfiles into HBase table
• Ideal setup should be
SET hive.hbase.bulk=true
INSERT OVERWRITE TABLE web_table SELECT ….
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Filter Pushdown
Architecting the Future of Big Data
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Filter Pushdown
• Idea is to pass down filter expressions to the storage layer to
minimize scanned data
• To access indexes at hdfs or hbase
• Example:
CREATE EXTERNAL TABLE users (userid LONG, email STRING, … )
STORED BY 'org.apache.hadoop.hive.hbase.HBaseStorageHandler’
WITH SERDEPROPERTIES ("hbase.columns.mapping" = ":key,…")
SELECT ... FROM users WHERE userid > 1000000 and email LIKE
‘%@gmail.com’;
-> scan.setStartRow(Bytes.toBytes(1000000))
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Filter Decomposition
• Optimizer pushes down the predicates to the query plan
• Storage handlers can negotiate with the Hive optimizer to
decompose the filter
x > 3 AND upper(y) = 'XYZ’
• Handle x > 3, send upper(y) = ’XYZ’ as residual for Hive
• Works with:
key = 3, key > 3, etc
key > 3 AND key < 100
• Only works against constant expressions
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Future of Hive + HBase
• Improve on schema / type mapping
• Fully secure Hive deployment options
• HBase bulk import improvements
• Filter pushdown: non key column filters
• Sortable signed numeric types in HBase
• Use HBase’s new typing API’s (upcoming in HBase)
• Integration with Phoenix / extract common modules, hbase-
sql ?
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References
• Type mapping / Filter Pushdown
– https://issues.apache.org/jira/browse/HIVE-1634
– https://issues.apache.org/jira/browse/HIVE-1226
– https://issues.apache.org/jira/browse/HIVE-1643
– https://issues.apache.org/jira/browse/HIVE-2815
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Thanks
Questions?
Architecting the Future of Big Data
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