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This article mainly introduces "the difference between Hive and Hbase". In daily operation, I believe that many people have doubts about the difference between Hive and Hbase. The editor consulted all kinds of materials and sorted out simple and easy-to-use methods of operation. I hope it will be helpful to answer the doubts about "the difference between Hive and Hbase". Next, please follow the editor to study!

Hive is born to simplify the writing of MapReduce programs. People who have done data analysis with MapReduce know that many analysis programs are basically the same except for different business logic. In this case, a programming interface such as Hive is needed. Hive itself does not store and calculate data, it completely depends on the table pure logic in HDFS and MapReduce,Hive, that is, the definition of tables, that is, table metadata. Using SQL to implement Hive is because SQL is familiar to everyone, the conversion cost is low, and the Pig with similar function is not SQL.

HBase is born for query, it provides a super-large memory Hash table by organizing the memory of all machines in the node, it needs to organize its own data structure, including disk and memory, but Hive does not do this. The table is a physical table in HBase, not a logical table. Search engines use it to store indexes to meet the real-time requirements of the query.

Hive, similar to CloudBase, is also a set of software that provides sql function of data warehouse based on hadoop distributed computing platform. It makes the summary of the massive data stored in hadoop and the impromptu query simple. Hive provides a set of QL query language, which is based on sql and is easy to use.

HBase is a distributed non-relational database based on column storage. The query efficiency of HBase is very high, mainly due to query and display results. Hive is a distributed relational database. It is mainly used for parallel and distributed processing of large amounts of data. All queries in hive except "select * from table;" need to be executed through Map\ Reduce. Because of Map\ Reduce, even a table with only one row and one column may take 8 or 9 seconds if it is not queried through select * from table;. But hive is good at dealing with large amounts of data. When there is a lot of data to deal with, and the Hadoop cluster is large enough, it shows its advantages.

Through the storage interface of hive, hive and Hbase can be used together.

1. Hive is a sql language, which operates the hdfs file system through a database. In order to simplify programming, the underlying calculation method is mapreduce.

2. Hive is a row-oriented database.

3. Hive itself does not store and calculate data, it completely depends on the table pure logic in HDFS and MapReduce,Hive.

4. HBase is created for query. It provides a very large memory Hash table by organizing the memory of all the machines in the node.

5. Hbase is not a relational database, but a column-oriented distributed database developed on hdfs, which does not support sql.

6. Hbase is a physical table, not a logical table. It provides a super-large memory hash table, through which the search engine stores the index to facilitate query operation.

7. Hbase is a column store.

Hive is for maintenance only, and it is really very slow to check!

This is because its underlying layer is distributed computing through mapreduce, such as hbase, hive, and pig. But on the whole, hadoop is relatively fast, because it carries out massive data storage and distributed computing, which is already very fast.

Hive and Hbase have different characteristics: hive is high-latency, structured and analysis-oriented, and hbase is low-latency, unstructured and programming-oriented. Hive data warehouses have high latency on hadoop.

HBase is located in the structured storage layer, Hadoop HDFS provides high-reliability underlying storage support for HBase, Hadoop MapReduce provides high-performance computing power for HBase, and Zookeeper provides stable service and failover mechanism for HBase.

In addition, Pig and Hive also provide high-level language support for HBase, which makes it very easy to process data statistics on HBase. Sqoop provides convenient RDBMS data import function for HBase, which makes it very convenient to migrate traditional database data to HBase.

At this point, the study of "the difference between Hive and Hbase" is over. I hope to be able to solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

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