Apache Hadoop Hive is an effective standard for SQL in Apache Hadoop. Hadoop Hive forms the front-end to parse SQL statements, to generate and optimize logical plans, translating these logical plans to physical plans which are then executed by various other MapReduce jobs. Hadoop Hive is designed to cater the data warehouse systems to ease the whole process of ad-hoc queries on Big Data stored in HDFS filesystems.
Cloudera Impala is an open source SQL query engine developed after Google. Cloudera Impala is a SQL engine that processes data which is stored in HBase and also in HDFS filesystems. Cloudera Impala uses Hive’s Megastore and has the ability to query the Hive tables directly as well. Unlike Hadoop Hive, Cloudera Impala can’t translate queries into MapReduce jobs which can execute them natively. Both of these, Apache Hadoop Hive and Cloudera Impala support the common standards HiveQL.
Apache Hive is undoubtedly the slowest in comparison with Cloudera Impala, but Apache Hive is a great option for heavy ETL jobs where reliability plays an important role. Impala is an open source SQL engine to process queries on huge volumes of data providing a very good performance over Apache Hadoop Hive.
Impala is way better than Hive but this does not qualify to say that it is a one-stop solution for all the Big Data problems. Impala is a memory intensive technology and performance driven technology. It does not run effectively for heavy data operations like joins as not everything can be pushed into the memory. If there is an application that has batch processing kind of needs, then those organizations should be opting Hive over Impala as Hive suites such a need more efficiently than Impala.
Big Data keeps getting bigger. It continues to pressurize existing data querying, processing, and analytic platforms to improve their capabilities without compromising on the quality and speed. A number of comparisons have been drawn and they often present contrasting results. Cloudera Impala and Apache Hive are being discussed as two fierce competitors vying for acceptance in database querying space. While Hadoop has clearly emerged as the favorite data warehousing tool, the Cloudera Impala vs Hive debate refuses to settle down.
Cloudera Impala is an open source Massively Parallel Processing (MPP) SQL engine. If the data is stored in a cluster of computers running Apache Hadoop, giving Hadoop’s dominance in data warehousing. Cloudera Impala is a wonderful choice for running queries on HDFS and Apache HBase. This doesn’t require the data to be moved or transformed prior to processing. Cloudera Impala is easily integrated with the whole of Hadoop ecosystem. Cloudera Impala’s unified resource management across frameworks makes it the standard for open source interactive business intelligence tasks. Cloudera Impala has the following two technologies that give other processing languages a run for their money:
Impala massively improves on the performance parameters as it eliminates the need to migrate huge data sets to dedicated processing systems or convert data formats prior to analysis. Some of the salient features of Cloudera Impala are given as below for ready reference:
Apache Hadoop Hive was initially developed by Facebook and it is a data warehouse infrastructure build over Hadoop platform for performing data-intensive tasks such as querying, analysis, processing, and visualization. It is versatile in its usage as it supports analysis of huge datasets stored in Hadoop’s HDFS and other compatible file systems such as Amazon S3. To keep the traditional database query designers interested, it provides an SQL – like language (HiveQL) with schema on reading and transparently converts queries to MapReduce, Apache Tez, and Spark jobs. Other features of Hive include:
Cloudera Impala has been identified as having a performance lead over Hadoop Hive by the benchmarks provided by both Cloudera and AMPLab. It is worthwhile to take a look at this constantly observed difference, the following reasons come to the rescue as possible causes for the same.
|Impala being a native query language, this avoids any possible startup overheads which are very frequently and commonly observed in MapReduce based jobs. Impala daemon processes are started at the boot time itself, making it ready to process a query always.||Every query in Hive has the common problem of a “cold start”.|
|Impala streams intermediate results between executors (which trades off scalability as such)||MapReduce materializes all intermediate results, thus enabling better scalability and fault tolerance (which has an adverse effect of slowing down the data processing).|
|Impala generates code for “big loops” during the Runtime||Hive generates query expressions at Compile time|
|Impala is meant for interactive computing||Hive is not an ideal for interactive computing|
|Impala is more like MPP database||Hive is a batch based Hadoop MapReduce|
|Impala does not support complex types||Hive supports complex types|
|Impala does not support fault tolerance||Hive is fault tolerant|
|When a data node goes down during the query execution, Impala starts all over again||When a data node goes down during the query execution, the output of the query will be produced as Hive is fault tolerant|
|NA||Hive transforms SQL queries into Apache Spark or Hadoop jobs, thus an ideal choice for long running ETL jobs.|
|Impala is 6-69 times faster than Hadoop Hive.
Impala has a lot of performance related advantages over Hive but depends on the task at hand.
|If you are starting something fresh then Cloudera Impala is the best choice out of the two||If you are considering of taking up an upgradation project then compatibility comes up as an important factor to rely upon, and hence Hadoop Hive will be your ideal choice.|
In this article, we have tried to understand what both of these technologies namely Hadoop Hive and Cloudera Impala are and also understood details about these technologies in detail. We have tried to showcase few differences between these two technologies but in practice, these are not two different competitors competing to show which one of them is the best, but each complements other in rarely good use cases but each of them is known for their characteristics as defined earlier.
In practical terms, both of Apache Hive and Cloudera Impala need not be competitors competing with each other. Both Hive Hadoop and Impala have a strong MapReduce foundation to execute queries. There can be situations that require to use both Hive and Impala together and get the best out of both the worlds – that is compatibility and performance. Hadoop Hive is more of the universal, versatile and the pluggable kind of language. Once the data integration and storage are answered, Cloudera Impala can unleash its brute processing power to give lightning fast analytic results.