By Nishant Garg
A functional advisor to understanding the seamless power of storing and dealing with high-volume, high-velocity info speedy and painlessly with HBase
About This Book
- Learn how one can use HBase successfully to shop and deal with unending quantities of data
- Discover the intricacies of HBase internals, schema designing, and lines like info scanning and filtration
- Optimize your great info administration and BI utilizing useful implementations
Who This e-book Is For
This publication is meant for builders and large facts engineers who need to know all approximately HBase at a hands-on point. For in-depth knowing, it might be beneficial to have a bit familiarity with HDFS and MapReduce programming strategies without previous event with HBase or comparable applied sciences. This booklet can also be for giant info fanatics and database builders who've labored with different NoSQL databases and now are looking to discover HBase as one other futuristic, scalable database resolution within the colossal info space.
What you'll Learn
- Realize the necessity for HBase
- Download and arrange HBase cluster
- Grasp info modeling recommendations in HBase and the way to accomplish CRUD operations on data
- Perform potent info scanning and knowledge filtration in HBase
- Understand info garage and replication in HBase
- Explore HBase counters, coprocessors, and MapReduce integration
- Get conversant in diverse consumers of HBase equivalent to relaxation and Kundera ORM
- Learn approximately cluster administration and function tuning in HBase
In Detail
With an example-oriented technique, this publication starts through supplying you with a step by step studying technique to without problems organize HBase clusters and layout schemas. steadily, you can be taken via complicated information modeling options and the intricacies of the HBase structure. furthermore, additionally, you will get familiar with the HBase shopper API and HBase shell. primarily, this ebook goals to supply you with a fantastic grounding within the NoSQL columnar database area and in addition is helping you are taking benefit of the true energy of HBase utilizing facts scans, filters, and the MapReduce framework. most significantly, the booklet additionally provide you with useful use situations overlaying a variety of HBase consumers, HBase cluster management, and function tuning.
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Additional resources for HBase Essentials
Sample text
Storing semi-structured data not only impacts the physical schema but also the logical schema of HBase. For the same reason, some features such as relational constraints are also not present in HBase. Similar to a typical RDBMS, tables are composed of rows and these rows are composed of columns. Rows in HBase are identified by a unique rowkey and are compared with each other at the byte level, which resembles a primary key in RDBMS. In HBase, columns are organized into column families. There is no restriction on the number of columns that can be grouped together in a single column family.
The following constructor can be used to create an instance of HTablePool: HTablePool() HTablePool(Configuration config, int maxSize) HTablePool(Configuration config, int maxSize, HTableInterfaceFactory tableFactory) The HTablePool class creates a pool with the HTable class instances with the configuration object, and setting the maxSize parameter defining the HTable instances to count a pool. getTable("Costumers"); CRUD operations A rowkey primarily represents each row uniquely in the HBase table, whereas other keys such as column family, timestamp, and so on are used to locate a piece of data in an HBase table.
A Put class instance is used to store data in an HBase table. toBytes("John")); HBase stores all the data, including the rowkey, in the form of a byte array and a Java utility class, bytes define various static utility methods for converting Java data types to and from a byte. Once a Put instance is created using the rowkey component, the next step is to add the data by using either of the following method definitions: add(byte[] family, byte[] qualifier, byte[] value) add(byte[] family, byte[] qualifier, long ts, byte[] value) add (byte[] family, ByteBuffer qualifier, long ts, ByteBuffer value) add (Cell kv) The add() option takes a column family along with an optional timestamp or one single cell as a parameter.