The Big Data Showdown: Apache Spark vs Impala

As the big data industry is growing, the tools and techniques associated with it are also increasing. There are various tools that have made it easy for professionals to analyze big data. However, the question that often arises is which among these tools is better? The same goes for Apache Spark and Impala. This article will try to find which one is better – Apache Spark or Impala.

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Apache Spark and Impala are two of the commonly-used tools in big data and there is an ongoing debate among the professionals who are divided on which one is better. Before we differentiate between Spark and Impala, let us understand a bit about them.

Let’s jump in:

What is Apache Spark?

Apache Spark is a fast and general engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing. It was originally developed at the AMPLab of the University of California in Berkley, and was later donated to Apache Software Foundation, which now maintains it. It’s written in Scala, Java, Python, and R; and works on most of the major OS, viz. Microsoft Windows, MacOS and Linux.

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What is Impala?

Impala is an open-source massively parallel processing (MPP) SQL query engine for data stored in a computer cluster running Apache Hadoop. It was developed by Cloudera and works in a cross-platform environment. The project was announced in 2012 and is inspired from the open-source equivalent of Google F1.

Apache Spark vs Impala

There have been a lot of questions whether Apache Spark is better than Impala or if it’s the other way round. Let us see some of the comparisons to find out:

Popularity

The popularity of a tool is important as no one would want to learn something that nobody in the industry uses. According to DB-engines ranking , Impala has a score of 12.79 with an overall rank of 31 and Spark has a score of 10.50 with an overall rank of 37. Though, they are not that apart, there is a difference in the popularity rankings which might give Impala an advantage.

The Score: Impala 1: Spark 0

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Fault Tolerance

Spark can run both short and long-running queries and recover from mid-query faults, while Impala is more focussed on the short queries and is not fault-tolerant.

The score: Impala 1: Spark 1

Usage

Impala is used for Business Intelligence (BI) projects because of the low latency that it provides. The reporting is done through some front-end tool like Tableau, and Pentaho. Spark can be used for analytics purposes where the professionals are inclined towards statistics as they can use R for designing the initial frames.

The Score: Impala 1: Spark 1

Multi-user performance

According to multi-user performance testing, it is seen that Impala has shown a performance that is 7 times faster than Apache Spark.

The Score: Impala 2: Spark 1

 

 

Hive support

Apache Spark supports Hive UDFs (user-defined functions). However, Impala, because of it uses a custom C++ runtime, does not support Hive UDFs.

The Score: Impala 2: Spark 2

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Throughput

Impala has a query throughput rate that is 7 times faster than Apache Spark.

The Score: Impala 3: Spark 2

 

 

Spark vs Impala – The Verdict

Though the above comparison puts Impala slightly above Spark in terms of performance, both do well in their respective areas. While Impala leads in BI-type queries, Spark performs extremely well in large analytical queries. So, it would be safe to say that Impala is not going to replace Spark soon or vice versa.