Features / Advantage / Disadvantage / use case of HBase

 Features of HBase NoSQL DB

  • Scalability: HBase supports scalability in both linear and modular form.
  • Sharding: HBase supports automatic sharding of tables. It is also configurable.
  • Distributed storage: HBase supports distributed storage like HDFS.
  • Consistency: It supports consistent read and write operations.
  • Failover support: HBase supports automatic failover.
  • API support: HBase supports Java APIs so clients can access it easily.
  • MapReduce support: HBase supports MapReduce for parallel processing of large volume of data. 
  • Back up support: HBase supports back up of Hadoop MapReduce jobs in HBase tables. 
  • Real time processing: It supports block cache and Bloom filters. So, real time query processing is easy. 
  • Apart from the above major features, HBase also supports REST-ful web services, jruby-based shell, Ganglia and JMX. So, HBase has a very strong presence in the NoSQL database world.

Advantages of HBase

  • Can store large data sets.
  • Database can be shared.
  • Cost-effective from gigabytes to petabytes.
  • High availability through failover and replication 

Disadvantages of HBase

  • No support SQL structure.
  • No transaction support.
  • Sorted only on key.
  • Memory issues on the cluster 

Some use cases

  • There are a lot of real-life implementations of HBase. Some of the important use cases are:
    • Use of HBase by Mozilla: They generally store all crash data in HBase
    • Use of HBase by Facebook: Facebook uses HBase storage to store real-time messages.
    • It is used whenever there is a need to write heavy applications.

Comments

Post a Comment

Popular posts from this blog

Why We Need Hadoop?

What is Hadoop? It is an open source framework or system which was fully developed by Java.  It is allow us to implement Big Data.  Hadoop can be used for storing and processing huge datasets with cluster of commodity hardware. Because its stores the data and processing in a distributed manner.  It made up of several components which entirely open source. Doug Cutting is the inventor of hadoop. He used elephant symbol. His child used to play with elephant toy. So he used that toy name as symbol and named for new invention. Why we need to go for Hadoop? 1. High Throughput and Low latency queries Less amount of time it will take to run/process real time queries or near real time queires. We can achieve high throughput in hadoop using below techniques.  Parallel Processing WORM Data Locality 2. Distributed computing A distributed computer system consists of multiple software components that are on multiple computers, but run as a single system.  The computers that ...
Read more

Job, Stage, Task in Spark

Image
Job, Stage, Task Job A job in Spark represents a complete computation triggered by an action (e.g., collect(), count(), saveAsTextFile()). When you call an action on a Spark RDD (Resilient Distributed Dataset) or DataFrame, Spark starts to execute the transformations defined in your code. A job consists of one or more stages, which are formed based on the DAG (Directed Acyclic Graph) of transformations that need to be executed to fulfill the action's requirements. Spark may optimize the execution plan by breaking the job into multiple stages to minimize data shuffling and improve performance. Stage In Apache Spark, a stage is a logical division of a Spark job's execution plan. Stages are formed during the process of executing a Spark job, which involves transforming data through a series of RDD (Resilient Distributed Dataset) or DataFrame operations and actions. When an action is triggered, Spark analyzes the DAG (Directed Acyclic Graph) of transformations that need to be execu...
Read more

Apache Spark Components

Image
Apache Spark is an open source framework for real-time processing. Also it is a powerful big data tool and used for various big data challenges.  Hadoop MapReduce is the best framework for processing data in batches. Spark could process data in real time and it is 100 times faster than Hadoop MapReduce in batch processing of large data sets.  Spark is able to achieve this speed through controlled partitioning. We could process that  partitioned  data with  minimal network traffic. Features Spark code can be written in Java, Scala, Python and R. Spark also supports and process structured and semi-structured data though Spark SQL. If it is absolutely necessary, then only spark will execute the code/function. This is called "Lazy Execution". Spark will be faster for processing the data because of its in-memory processing. Apache Spark is a separate tool and it has its own cluster. We can use Hadoop to implement Spark. Spark Components     ...
Read more