Introduction to Hive Alternatives
Before we discuss the alternatives of HIVE. Let’s first understand what is a HIVE? So, HIVE is basically a data warehousing tool that is developed on top of HDFS (Hadoop Distributed File System). It is used for giving an SQL like query interface to query data which is stored in various files integrated with Hadoop. It converts SQL like queries to Map Reduce jobs which help in easy execution of large volumes of data.
Below are some of the features of Hive:
- Like SQL it has its own declarative language called HiveQL.
- It has a table structure similar to tables in the Relational database and it also provides ETL (extract/support /load) support.
- An interesting feature is that it allows the conversion of format from within the HIVE.
Limitation of Hive Alternatives
Let’s know a few limitations of Hive:
- It is not designed for OLTP (Online Transaction Processing) but supports OLAP (Online Analytical Processing).
- One important limitation is that it does not support updates and deletes.
- In Hive Subqueries are also not supported.
5 Important Hive Alternatives
Below we are going to discuss five important alternatives of HIVE present in the market:
1. Apache Impala
It is an open-source parallel processing SQL query engine for data stored in a computer cluster running Apache Hadoop. It was announced in the year October 2012. Below are the salient features of Apache Impala as an alternative to HIVE.
- Impala is a good choice for people running SQL queries on Hadoop and Apache HBase without transforming the data as it is not required to transform or move the data, unlike HIVE.
- Another difference between these two is the generation of query expressions. Impala generates them at runtime using llvm while HIVE generates them at compile time.
- Hive Queries has a problem of cold start which is not the case of Impala queries as in Impala daemon processes are started in boot time itself always ready to process a query due to which it avoids the cold start problem.
- Impala recognizes Hadoop File formats, Hadoop security, ODBC driver.
- The main USP of impala is the brute force of parallel processing. So, Impala is a better alternative if one is starting a new project.
2. Presto DB
Presto is another alternative for HIVE developed by facebook. Its USP is it can even query data from multiple sources within a single query. Below are the salient features of PrestoDB as an alternative to HIVE.
- Presto is an in-memory distributed SQL query engine which is It is also very fast as the query engine of Presto is fast and well suited for interactive analysis.
- The USP for Presto over others is its plug and play model with different data sources. Due to this plug and play model joining queries across different data sources is very easy with presto.
- In Presto small dimension join tables have been made faster. Presto excels with most other distributed query engines.
- Presto is not appropriate for large fact joins as it does not leverage disk and use memory for processing.
- One more important point for presto is its resource allocation. It has a priority queue based resource allocation.
- One trade-off for good performance in Presto is that UDF support is not available in presto due to which one has to write one’s own function which increases the overhead as it has to be built exclusively for presto and hampers the interoperability.
3. Spark SQL
It is a module for also structured data processing and also open-source. It can also act as a distributed SQL query engine and also one unique part of this is it provides programming abstraction known as data frames. It was first released in 2014 developed by Apache Software Foundation. Below are some of the salient features of Spark SQL as an alternative to HIVE.
- The good thing about Spark SQL is that it can be implemented in Java, Scala, Python, and R language whereas HIVE can be implemented in Java Language.
- There is complete similarity in Primary database model between HIVE and Spark as for both primary database model is Relational DBMS.
- It is also similar to HIVE as both support the Key-Value store as an additional database model.
- It has pre-defined data types such as float and date.
- It supports SQL as it possesses DML and DDL statements.
- Unlike HIVE which supports JDBC, ODBC, and Thrift, Spark SQL only supports JDBC and ODBC.
- Spark SQL uses spark core for storing data into different nodes.
- Another major difference between spark and HIVE is replication methods: There is selective replication factor in HIVE for storing redundant data on multiple nodes but no replication factor is available in Spark SQL.
- In Spark SQL there are no access rights for Users whereas in Apache Hive we have access rights for users, groups.
- It does not support a transactional table and no support for char type.
It is an open-source SQL query engine which is written in Scala. The interesting fact of Shark is instead of using Map-Reduce to execute its queries it uses its own sets of worker nodes. Below are some of the features of Shark:
- It uses a Command-line client.
- It offers interoperability with Hive for schema sharing.
- It provides support for existing hive extensions such as UDFs.
It is not very famous yet but it provides an alternative to HIVE.
5. BigSQL by IBM
It is provided by Big Blue(IBM). IBM has its own Hadoop distribution called Big Insights. So, Big SQL is offered as part of it. It is not open source as it is provided by IBM. Some of the things they provide are as below:
- They support both JDBC and OJDBC drivers.
- They provide SQL support
- They can be used to query data from HDFS.
This is a guide to Hive Alternatives. Here we discuss features, limitation and 5 important Hive Alternatives. You can also go through our other related articles to learn more-