1. HCatalog
Table Management for Hadoop
Alan F. Gates

2. Motivation: Data Sharing is Hard
This is analyst Joe, he uses
This is programmer Bob, he Hive to build reports and
uses Pig to crunch data. answer ad-hoc queries.
Joe, I need
today’s data
Ok
Photo Credit: totalAldo via Flickr
Hmm, is it done yet? Where is it? What format did
you use to store it today? Is it compressed? And
can you help me load it into Hive, I can never
remember all the parameters I have to pass that
alter table command.
Dude, we need HCatalog

3. More Motivation: Each tool requires its own
Translator
Pig Hive Map Reduce
Hive HCatLoader HCatSerDe RCFile Custom
HCatInputFormat
Custom Columnar Custom
Columnar Input Input
Loader SerDe SerDe
Loader Format Format
HCatalog
RCFile Custom
StorageDriver StorageDriver
Custom
RCFile
Format

4. End User Example
raw = load „/rawevents/20100819/data‟ using MyLoader()
as (ts:long, user:chararray, url:chararray);
botless = filter raw by NotABot(user);
…
store output into „/processedevents/20100819/data‟;
Processedevents consumers must be manually informed by producer that data is
available, or poll on HDFS (= bad for the NameNode)
raw = load „rawevents‟ using HCatLoader();
botless = filter raw by date = „20100819‟ and NotABot(user);
…
store output into „processedevents‟
using HCatStorage(“date=20100819”);
Processedevents consumers will be notified by HCatalog data is available and can
then start their jobs

5. Command Line for DDL
• Uses Hive SQL
• Create, drop, alter table
• CREATE TABLE employee (
emp_id INT,
emp_name STRING,
emp_start_date STRING,
emp_gender STRING)
PARTITIONED BY (
emp_country STRING,
emp_state STRING)
STORED AS RCFILE
tblproperties(
'hcat.isd'='RCFileInputDriver',
'hcat.osd'='RCFileOutputDriver');

6. Manages Data Format and Schema Changes
• Allows columns to be appended to tables in new partitions
− no need to change existing data
− fields not present in old data will be read as null
− must do „alter table add column‟ first
• Allows storage format changes
− no need to change existing data, HCatalog will handle reading each
partition in the appropriate format
− all new partitions will be written in current format

7. Security
• Uses underlying storage permissions to determine
authorization
− Currently only works with HDFS based storage
− If user can read from the HDFS directory, then he can read the table
− If user can write to the HDFS directory, then he can write to the table
− If the user can write to the database level directory, he can create and
drop tables
− Allows users to define which group to create table as so table access
can be controlled by Unix group
• Authentication done via kerberos

8. Metadata Architecture
HCatLoader HCatStorage HTTP
HCatInputFormat HCatOutputFormat CLI Notification
Hive metadata interface
Thrift
server RDBMS
= Current HCatalog
= Hive
= Future HCatalog

9. Storage Architecture
HCatLoader HCatStorage
HCatInputFormat HCatOutputFormat
Input Output
StorageDriver StorageDriver
HDFS HBase

10. Project Status
• HCatalog was accepted to the Apache Incubator last March
• 0.2 released in October, includes:
− Read/write from Pig
− Read/write from MapReduce
− Read/write from Hive
− StorageDrivers for RCFile
− Notification via JMS when data is available
− Store to multiple partitions simultaneously
− Import/Export tools

11. HCatalog 0.3
• Plan to release mid-December
• Adds a Binary type (to Hive and HCatalog)
• Storage drivers for JSON and text
• Improved integration with Hive for custom storage formats
• Web services interface

12. Future Plans
• Support for HBase and other data sources for storage
• RCFile compression improvements
• High Availability for Thrift server
• Data management interfaces for archivers, cleaners, etc.
• Additional metadata storage:
− statistics
− lineage/provenance
− user tags

13. Get Involved
• incubator.apache.org/hcatalog
• Join the mailing lists
− User list: hcatalog-user@incubator.apache.org
− Dev list: hcatalog-dev@incubator.apache.org

14. Questions?