Hive and Impala are SQL based open source frameworks for querying massive datasets. These are common technologies used by Big Data Analysts. Even though there are many similarities but both these technologies have their own unique features. Now, we will compare “Hive vs Impala” and also will discuss how the performance of Impala is better than Hive.
Some important comparisons between Hive and Impala are given below-
Hive
Hive was developed in Facebook around year 2007.
It was created as a Data Warehouse (DW) Solution on Hadoop. It is a Batch Solution. So, you can just do fire and forget kind of use cases Hive is based on MR/Spark/Tez architecture. It is fault tolerant and it will retry the tasks that are failing and try to recover them.
Hive is Java based. Java is the prerequisite before Hive is installed. In Hive, the performance is not so good because Java is a application programming language. In Hive, on-demand JVM processes are created.
Hive UDFs (User Defined Functions) are based on Java Primarily. It is not a columnar SQL. It is a Batch Data Solution.
Hive has support for SerDes. SerDes is Extending Hive framework with some new format. Updates in Hive are allowed, may be using Optimized Row Columnar (ORC) format. It is used for long running jobs
If you want to do sentiment analysis and log file analysis, then you will go for Hive.
Impala
Impala was developed in Cloudera around year 2012
It was created with the intention to perform ad-hoc Querying on Big Data
Impala is for interactive use cases.So, user presence is required. It is based on Massively Parallel Processing (MPP) architecture.
Impala is not fault tolerant. That’s why it is used for interactive querying and user presence is required.
It is C++ based. In general, performance is better in Impala because C++ is a system programming language (Closer to hardware)
Impala is having Impala Daemons which are always on and always running. Impala UDFs are also based on C++ apart from Java.
It is based on Google Dremel White Paper. It will do columnar SQL processing, which means it will create tree like structure for different columns.
Impala is not having SerDes (Serializer and Dserializer) feature.
No Updates are allowed in Impala. It is used for short lived queries.
If you want to do BI tools Integration or interactive querying then you will use Impala.
Hope this article gives a better understanding of when to choose Hive and when to choose Impala.
Thank you!
