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HBase has established itself as a crucial tool for various organizations, including PayPal, to store and retrieve records in near real …
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What is HBase?
The Apache HBase project's goal is the hosting of very large tables -- billions of rows X millions of columns -- atop clusters of commodity hardware. Apache HBase is an open-source, distributed, versioned, non-relational database modeled after Google's Bigtable.
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What is HBase?
The Apache HBase project's goal is the hosting of very large tables -- billions of rows X millions of columns -- atop clusters of commodity hardware. Apache HBase is an open-source, distributed, versioned, non-relational database modeled after Google's Bigtable.
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The Apache HBase project's goal is the hosting of very large tables -- billions of rows X millions of columns -- atop clusters of commodity hardware. Apache HBase is an open-source, distributed, versioned, non-relational database modeled after Google's Bigtable.
Reviewers rate Scalability highest, with a score of 8.6.
The most common users of HBase are from Enterprises (1,001+ employees).
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HBase has established itself as a crucial tool for various organizations, including PayPal, to store and retrieve records in near real time. Users have found that HBase excels in analytical use cases by providing faster lookup of records with consistent reads and writes, making it ideal for handling large datasets. It allows for faster querying of records compared to other NoSQL databases, resulting in improved data access and analysis capabilities. The ease of installation and configuration, thanks to its integration with the HDP Hortonworks stack, is another advantage that users appreciate.
One significant use case for HBase is as a data store for streaming data ingested through mechanisms like Apache NiFi, Apache Storm, Apache Spark Streaming, Apache Flink, and Streaming Analytics Manager. This allows organizations to efficiently manage and process continuous streams of data. Furthermore, HBase's ability to store structured, semi-structured, and unstructured data without requiring a pre-defined schema makes it a versatile choice for a range of applications.
Customers across industries have leveraged HBase successfully for their specific needs. In the retail sector, it serves as a datastore for product catalogs, session management systems, and revenue-generating platforms. Additionally, businesses involved in advertising and location analytics rely on HBase to generate locational information efficiently. Its scalability and read performance with avro data containing geospatial information make HBase preferable over alternatives like Cassandra.
HBase also plays a vital role in managing data within Apache Hadoop systems. It is used to create master data sets and reconcile conflicting data. Moreover, HBase serves as a secondary layer of storage that consolidates updates from upstream key-value stores.
While users highly recommend HBase for its data model consistency, scalability, and well-documented features, they do acknowledge the operational overhead associated with deploying and managing clusters. Nonetheless, this does not overshadow the significant benefits that organizations derive from using HBase to solve scalability and management issues related to multi-terabyte applications.
HBase is recommended for handling huge amounts of data and integrating with other tools. Users find HBase to be a good choice for scenarios requiring streaming ingest, fast lookups, and processing massive datasets. Its integration capabilities with other tools make it a valuable asset for organizations dealing with vast amounts of data.
HBase is also highly regarded for its real-time reporting capabilities over big data and seamless integration with business intelligence (BI) tools. Users recommend HBase as a reliable NoSQL datastore specifically designed to handle big data loads. It serves as an effective solution for storing unstructured or semi-structured data while providing easy integration with frontend applications.
Another common recommendation is to consider HBase's native integration with Hadoop and other data access engines. Users find HBase helpful for storing and processing non-relational data efficiently. Additionally, they recommend it as a reliable option for data storage and provision to other applications, making it suitable for various use cases.
It is important to evaluate HBase when considering NoSQL databases, as it offers unique benefits such as amazing structured/unstructured data storage capabilities and support for parallel programming. Users suggest utilizing HBase for specific use cases where large amounts of similar data need to be stored and accessed easily.
Lastly, users emphasize the importance of proper data modeling and workload tuning for successful implementation of HBase. They advise against using HBase for full table scan workloads and suggest considering relational databases when applicable. Additionally, they encourage the use of HBase for OLAP and OLTP use cases, highlighting its suitability for handling huge datasets and analytical processing needs.
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March 16, 2019
HBase as the brother of big data
I use HBase because it is a NoSQL database and it is open sourced and can store big data. We can store any structured, semi-structured and unstructured data easily. One other major benefit is, it is a columnar database so no need to specify any schema. I generally use it when I store the streaming data, the analysis is also faster after connecting the HBase with Spark. HBase is a mature database so we can connect HBase with various execution engine and other component using JDBC.
- HBase stores the big data in a great manner and it is horizontally scalable.
- Another major reason is security, we can secure the HBase database using Atlas, Ranger.
- Store any format of data like structured, semi-structured and unstructured.
- Consistency
- Strongly consistent reads and writes are provided by HBase, we use it for high-speed requirements if we do not need RDBMS-supported features such as full transaction support or typed columns.
- There are very few commands in HBase.
- Stored procedures functionality is not available so it should be implemented.
- HBase is CPU and Memory intensive with large sequential input or output access while as Map Reduce jobs are primarily input or output bound with fixed memory. HBase integrated with Map-reduce jobs will result in random latencies.
December 14, 2018
An Amazing Experience
HBase was used as a datastore for the retails product catalog and session maangement
- Scalable and truly non-relational data
- HBase operations run in real-time on its database rather than MapReduce jobs
- Scales linearly to support billions of rows with millions of columns
- Difficult for people who are building custom tools for SQL like purposes to understand HBase
- Cannot be used for transactional datasets
December 13, 2018
HBASE!!!
HBase is used as part of the company's main revenue generating platform. We're using it store data with usages of mapreduce, generates locational information for advertising business and location analytics. Storage wise, it made sense to use HBASE over Cassandra, as well as for read performance with avro data with geospatial information in the data
- Excellent for read performance
- Great store of file format of avro
- Easy integration into mapreduce
- Replication ability
- Write performance
- Performance support for parquet file format. supports, but performance wise still not there
- API / library availability for spark, rather than creating a new library for it
April 19, 2018
No SQL Database to Support Near Real Time Analytics
HBase was used in my previous organization(PayPal) where we needed a database for storing and retrieving records in near real time. It was used within consumer analytics and other sub-teams. It supported our near real-time use analytical cases by proving a faster lookup of records with consistency reads/writes. Apart from that, helped in querying the records much faster than other NoSQL databases.
- Faster lookup of records using the row keys. It helped to fetch thousands of records in a much faster way using the row keys
- As it is a columnar data store, helped us to improve the query performance and aggregations
- Sharding helps us to optimize the data storage and retrieval. HBase provides automatic or manually sharding of tables.
- Dynamic addition of columns and column family helped us to modify the schema with ease.
- Identified issues with Hmaster when handling a huge number of nodes
- Cannot have multiple indexes as row key is the only column which could be indexed.
- HBase does not support partial row keys which limit its query performance.
April 06, 2018
HBase, The Only Enterprise NoSQL Choice
HBase is being used by multiple organizations and internally it is used company-wide. it solves a large range of problems and provides unique solutions when we need a NoSQL store.
HBase provides the best of breed solutions for any NoSQL storage needs. One of the main important features is it is part of the HDP Hortonworks stack so it is installed by default so there's nothing else to install or configure. It is easy to administer with Ambari and scales to any size I need. It runs on top of HDFS so my data is safe, secure and scalable.
I use it as a store for data that is ingested via various streaming mechanisms including Apache NiFi, Apache Storm, Apache Spark Streaming, Apache Flink and Streaming Analytics Manager. It provides an easy key-value type store with fast scans for data access. I also run Apache Phoenix on top to provide a fast clean SQL interface to all of my data.
HBase provides the best of breed solutions for any NoSQL storage needs. One of the main important features is it is part of the HDP Hortonworks stack so it is installed by default so there's nothing else to install or configure. It is easy to administer with Ambari and scales to any size I need. It runs on top of HDFS so my data is safe, secure and scalable.
I use it as a store for data that is ingested via various streaming mechanisms including Apache NiFi, Apache Storm, Apache Spark Streaming, Apache Flink and Streaming Analytics Manager. It provides an easy key-value type store with fast scans for data access. I also run Apache Phoenix on top to provide a fast clean SQL interface to all of my data.
- Scalability. HBase can scale to trillions of records.
- Fast. HBase is extremely fast to scan values or retrieve individual records by key.
- HBase can be accessed by standard SQL via Apache Phoenix.
- Integrated. I can easily store and retrieve data from HBase using Apache Spark.
- It is easy to set up DR and backups.
- Ingest. It is easy to ingest data into HBase via shell, Java, Apache NiFi, Storm, Spark, Flink, Python and other means.
- Not for small data
- Requires a cluster
September 13, 2017
HBase
HBase solves problems of scalability and management of multi-terabyte applications. It makes scaling to +1 nodes very easy, especially through Ambari. It is built with fault tolerance and availability in mind. You can use it on a single node but it shines on multi-node infrastructure. With high data access speed and resiliency, I wouldn't recommend any other NoSQL database for general use.
- HBase data access and retrieval only gets better with larger scale.
- Fault tolerance is built in, if you have unreliable hardware, HBase will make every effort to keep your data online.
- Extremely fast key lookups and write throughput.
- Multi-tenancy is still work in progress
- Usability and beginner friendliness
- It has a bad reputation of being complex
April 14, 2017
HBase - good for UI performance from a Hadoop cluster
HBase makes it possible to provide sub-second UI responsiveness when querying very large data sets. This is in contrast to something like Hive, which could take many minutes.
- Very fast query capability
- Resilient: by leveraging hdfs, hbase can handle server failure pretty well
- Very schema dependent - you have to carefully choose your schema and key strategy in order to get good distribution and performance.
- Over aggressive rebalancing - if you have to bounce your system - for example - hbase will spend quite a while trying to rebalance all the data as each server comes online.
April 04, 2017
Support for HBase
It is used as a data store that consolidates the updates from the upstream key-value store where upstream store only stores the updates that meet the high qps and low latency. HBase is the secondary layer of the storage that consolidate all the updates for a given row key and serves as a upstream for hive table.
- Good write throughput
- Good horizontal scalability
- Easy to operate on
- Better tool for investigating the key-value content for data validation.
- Better tool for row key monitoring since our key contains timestamps.
- Better tool for system-level metric monitoring.
June 20, 2016
HBase - a scaleable, consistent data store
We use HBase as a secondary data store. We chose it mainly for its strongly consistent data model, and scalability. It has a pretty good documentation and a strong and active developer community that is still growing. The main downside is its many moving parts and operational overhead of deploying and managing clusters.
- Scalability
- Strong consistency
- SQL layer
- Too many processes
- Difficult to manage many clusters
November 25, 2015
Apache HBase: Through the Looking Glass!
Apache HBase was used for mastering solutions, for creating master data sets and reconciling conflicting data coming to Apache Hadoop systems.
- Apache HBase is a widely used java based distributed NoSQL environment on Apache Hadoop.
- While there has been growing interest and efforts in in memory computing, there are investments on Apache Hadoop (or hadoop provider variants) across domains. So that is a large market.
- I worked on HBase for applications which needed to provide strong consistency and interact with Apache Hadoop.
- You could encounter issues like region is not online or NotServingException or region server going down, out of memory errors.
- As HBase works with Zookeeper, care needs to be taken it is correctly set up. Most issues pertain usually to environment setup, configuration, shared load on system or maintenance.
- The performance across workloads when evaluated against other NoSQL variants was not best in class, this is most times okay, but can be improved.
- If you use Apache HBase, and want to upgrade it for some features then you might need to do a compatibility check against your Apache Hadoop and Apache HBase versions, there are dependency to think about.
- The HBase master slave becomes the single point of failure, and may not be a preferred design.It is not highly available system.
- Last I checked it did not have well tested easy integrations with Spark, and that can help.