Hadoop was seen as a silver bullet for many companies, but recently there has been an increase in critical headlines like:

  1. Hadoop Has Failed Us, Tech Experts Say
  2. You’re doing Hadoop and Spark wrong, and they will probably fail
  3. Has Hadoop Failed? That’s the Wrong Question

The problem

Dig behind the headlines, and a major issue is the inability for users to query data in Hadoop in the manner they are used to with commercial database products.

From the Datanami article:

  • Hadoop’s strengths lie in serving as a cheap storage repository and for processing ETL batch workloads, Johnson says. But it’s ill-suited for running interactive, user-facing applications
  • It’s better than a data warehouse in that have all the raw data there, but it’s a lot worse in that it’s so slow
  • “At the Hive layer, it’s kind of OK. But people think they’re going to use Hadoop for data warehouse…are pretty surprised that this hot new technology is 10x slower that what they’re using before,” Johnson says. “[Kudo, Impala, and Presto] are much better than Hive. But they are still pretty far behind where people would like them to be.”

The Register article based on a Gartner research talk recognises Hadoop’s strength for ETL processing, but highlights the issues with SQL-handling on Hadoop.

The Podium Data article states “Hadoop is terrible as a relational database”, and “Hadoop failed only in the sense that inflated expectations could never be met compared to mature commercial offerings.”

“The Growing Need for SQL for Hadoop” talks about the need for SQL for Hadoop. The ideal is to be “on Hadoop”, and thus processing data within the Hadoop cluster, rather than “off Hadoop” where data has to be extracted from Hadoop for processing.

Similarly, Rick van der Lans talks about “What Do You Mean, SQL Can’t Do Big Data?”, emphasising the need for SQL solutions when working with big data platforms.

RCA of the problem

There can be many reasons for current SQL-on-hadoop products not being performant.

Possibilities include:

  • overhead of starting and stopping processes for interactive workloads – to run relatively simple queries quickly, you need to reduce latency. If you have a lot of overhead for starting and stopping containers to run tasks, that is a big impediment to interactive usage, even if the actual processing is very efficient
  • product immaturity – a lot of commercial databases have built on the shoulders of giants. For example, this wiki lists a set of products that derive from PostgreSQL, including Greenplum, Netezza, ParAccel, Redshift, Vertica. This gives these products a great start in avoiding a lot of mistakes made in the past, particularly in areas such as SQL optimisation. In contrast, most of the SQL-on-hadoop products are built from scratch, and so developers have to learn and solve problems that were long-since addressed in commercial database products. That is why we see great projects like Presto only starting to add a cost-based optimiser now, and Impala not being able to handle a significant number of TPC-DS queries (which is why Impala TPC-DS benchmarks tend to show less than 80 queries, rather than the full 99 from the query set).
  • evolution from batch processing – if a product like Hive starts off based on Map-Reduce, its developers won’t start working on incremental improvements to latency, as they won’t have any effect. Similarly, if Hive is then adopted for a lot of batch processing, there is less incentive to work on reducing latency. Hive 2 with LLAP project aims to improve matters in this area, but in benchmarks such as this AtScale one reported by Datanami it still lags behind Impala and SparkSQL.


Whilst benchmarks show that SQL on Hadoop solutions like Hive, Impala and SparkSQL are all continually improving, they still cannot provide the performance that business users need.

Kognitio have an SQL engine originally developed for standalone clusters of commodity servers, and used by a host of enterprise companies. Due to this heritage, the software has a proven history of working effectively with tools like Tableau and MicroStrategy, and delivering leading SQL performance with concurrent query workloads – just the sort of problems that people are currently trying to address with data in Hadoop. The Kognitio SQL engine has been migrated to Hadoop, and could be the solution a lot of users of Hive, Impala and SparkSQL need today.

It has the following attributes:

  • free to use with no limits on scalability, functionality, or duration of use
  • mature in terms of query optimisation and functionality
  • performant, particularly with concurrent SQL query workloads
  • can be used both on-premise and in the cloud

For further information about Kognitio On Hadoop, try:


This post first appeared on LinkedIn on March 23, 2017.