HCatalog Tutorial for Beginners
Do you want to learn the other important component of the Hadoop ecosystem, that is, HCatalog? HCatalog is the table storage management tool for Apache Hadoop, which exposes the tabular data of Apache Hive metastore to the other. In this HCatalog tutorial, you will learn the basics of one of the Hadoop ecosystem components, HCatalog. This HCatalog Tutorial will explain what HCatalog is, why we need it, HCatalog architecture, and many more. Let us have a look at the topics that we are going to explore in this HCatalog tutorial article.
- What is HCatalog?
- Why do we use HCatalog?
- HCatalog Architecture
- Features of HCatalog
- Applications of HCatalog
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What is HCatalog?
HCatalog is basically a table and storage management layer for Apache Hadoop which enables users having different data processing tools such as Pig, MapReduce to read and write data on the grid with ease.
The HCatalog’s table abstraction presents the relational view of Hadoop Distributed File System (HDFS) data to the users. It ensures to the user that they should not have to worry about the location and the format in which their data is stored. It can store data in the format like RCFile format, SequenceFiles, text files, or ORC files.
HCatalog provides support for reading and writing files in any of the formats for which we can write the SerDe (serializer-deserializer). HCatalog, by default, provides support for RCFile, SequenceFile, CSV, JSON, and ORC file formats.
For using a custom format, we have to provide the InputFormat, OutputFormat, and the SerDe.
Why do we use HCatalog?
HCatalog is mainly used for the following three reasons:
1. Integrating Hadoop with everything
Apache Hadoop, because of its reliable storage and distributed processing, opens up a lot of opportunities for the businesses. However, in order to increase the adoption of Hadoop, it must work with existing tools. Also, its adoption can be fuel if it serves as an input into our analytics platform or if we can integrate it with our operational data stores and the web applications. Organizations want to enjoy the value of Hadoop without learning an entirely new toolset. The REST services open up the platform to the businesses with the familiar API and the SQL-like language. The Enterprise data management system uses HCatalog to integrate more deeply with the Apache Hadoop platform.
2. Enabling right tool for the right job
The Hadoop ecosystem contains various tools for data processing, such as MapReduce, Hive, Pig. Although these tools don’t require any metadata but they can still get benefit from it if it is present.
The workflow where we load the data and normalize it using Hadoop MapReduce or Pig and then analyze it through Apache Hive is very common.
If these tools share one metastore, then users of each tool can have immediate access to the data created with the other tool. Thus, there is no need for loading or transfer steps.
3. Capture the processing states in order to enable sharing
HCatalog can publish our analytics results. The other programmer can easily access our analytics platform through “REST”. The schemas that are published by us were also useful to the other data scientists. The other data scientists can use our discoveries as an input into the subsequent discovery.
HCatalog is built on the top of Hive metastore and incorporates Hive DDL commands. It provides read and write interfaces for MapReduce and Pig. It uses the command line interface of the Apache Hive in order to issue the data definition and the metadata exploration commands.
HCatalog Interface for Pig
HCatalog interface for Pig consists of the HCatLoader and the HCatStorer. HCatLoader implements the Pig load interface. It accepts the table to read data from. We can indicate the partitions which have to be scanned by immediately following a load statement with the partition filter statement. HCatLoader is implemented on the top of the HCatInputFormat.
HCatStorer implements the Pig store interfaces. It accepts the table to write to and optionally the specification of the partition keys to create the new partition. We can write to the single partition by specifying partition key(s) and value(s) in a STORE clause. We can write to the multiple partitions if partition key(s) are the columns in data that is being stored. HCatStorer is implemented on the top of the HCatOutputFormat.
HCatalog Interface for MapReduce
The HCatInputFormat and the HCatOutputFormat is the implementation of the Hadoop InputFormat and the OutputFormat.
HCatInputFormat accepts the table to read the data from and optionally the selection predicate in order to indicate which partitions to scan.
HCatOutputFormat accepts the table to write to and optionally the specification of the partition keys to create the new partition. We can write to the single partition by specifying a partition key(s) and value(s) in a setOutput method. We can write to the multiple partitions if partition key(s) are the columns in the data that is being stored.
Note: HCatalog doesn’t have any Hive-specific interface. Since HCatalog uses the Hive’s metastore so Hive can directly read data in HCatalog.
HCatalog Data Model
HCatalog presents the relational view of data. The data is stored in the tables and we can place these tables in the databases.
The tables can also be hash partitioned on the basis of one or more keys. This means for the given value of a key (or for the set of keys) there will be one partition that contains all the rows with that value (or the set of values).
For example, if the table is partitioned on the basis of date and there are a total of three days of data in the table, then there will be a total of three partitions in the table. We can add new partitions to a table or drop a partition from a table.
The partitioned tables do not have any partitions at create time. On the other hand, unpartitioned tables have one default partition that must be created at the time of table creation. When the partition is dropper there is no guaranteed read consistency.
Partitions contain the records. Once we create a partition, then records cannot be added to it, removed from it, or updated in it.
Partitions are not hierarchical. They are multi-dimensional. The records are divided into the columns. The columns have the name and the data type. HCatalog supports the same datatypes as Apache Hive.
- It assists the integration with the other tools and provides read and write interfaces for MapReduce, Pig, and Hive.
- HCatalog provides the shared schema and data types for the Hadoop tools. So we don’t have to type the data structures in each program explicitly.
- HCatalog exposes the information as a Rest Interface for the external data access.
- It can also integrate with the Sqoop and the relational databases like SQL Server and Oracle
- HCatalog provides APIs and the web service wrapper for accessing the metadata in the Hive metastore.
- HCatalog also exposes the REST interface so that we can create the custom tools and the applications in order to interact with the Hadoop data structures.
- It offers Kerberos based authentication.
In short, we can say that HCatalog is the table storage management tool for Apache Hadoop, which exposes the tabular data of Apache Hive metastore to the other Hadoop applications. It provides read and write interfaces for MapReduce, Hive, and Pig. It ensures users not to worry about the location and the format in which their data is stored. Finally the HCatalog Tutorial article had explained the HCatalog architecture, features, data model, and many more.
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