Rick van der Lans explains why it’s important to evaluate the differences in the technologies that make it possible to access Hadoop data using SQL.
In the world of Hadoop and NoSQL, the spotlight is now on SQL-on-Hadoop engines. Today, many different engines are available, making it hard for organizations to choose. This article presents some important requirements to consider when selecting one of these engines.
With SQL-on-Hadoop technologies, it’s possible to access big data stored in Hadoop by using the familiar SQL language. Users can plug in almost any reporting or analytical tool to analyze and study the data. Before SQL-on-Hadoop, accessing big data was restricted to the happy few. You had to have in-depth knowledge of technical application programming interfaces, such as the ones for the Hadoop Distributed File System, MapReduce or HBase, to work with the data. Now, thanks to SQL-on-Hadoop, everyone can use his favorite tool. For an organization, that opens up big data to a much larger audience, which can increase the return on its big data investment.
The first SQL-on-Hadoop engine was Apache Hive, but during the last 12 months, many new ones have been released. These include CitusDB, Cloudera Impala, Concurrent Lingual, Hadapt, InfiniDB, JethroData, MammothDB, Apache Drill, MemSQL, Pivotal HawQ, Progress DataDirect, ScleraDB, Simba and Splice Machine.
In addition to these implementations, all the data virtualization servers should be included because they also offer SQL access to Hadoop data. In fact, they are designed to access all kinds of data sources, including Hadoop, and they allow different data sources to be integrated. Examples of data virtualization servers are Cirro Data Hub, Cisco/Composite Information Server, Denodo Platform, Informatica Data Services, Red Hat JBoss Data Virtualization and Stone Bond Enterprise Enabler Virtuoso.
And, of course, there are a few SQL database management systems that support polyglot persistence. This means that they can store data in their own native SQL database or in Hadoop; by doing so, they also offer SQL access to Hadoop data. Examples are EMC/Greenplum UAP, HP Vertica (on MapR), Microsoft PolyBase, Actian ParAccel and Teradata Aster Database (via SQL-H).
SQL equality on Hadoop?
In other words, organizations can choose from a wide range of SQL-on-Hadoop engines. But which one should be selected? Or are they so alike that it doesn’t matter which one is picked?
The answer is that it does matter, because not all of these technologies are created equal. On the outside, they all look the same, but internally they are very different. For example, CitusDB knows where all the data is stored and uses that knowledge to access the data as efficiently as possible. JethroData stores indexes to get direct access to data, and Splice Machine offers a transactional SQL interface.
Selecting the right SQL-on-Hadoop technology requires a detailed study. To get started, you should evaluate the following requirements before selecting one of the available engines.
SQL dialect. The richer the SQL dialect supported, the wider the range of applications that can benefit from it. In addition, the richer the dialect, the more query processing can be pushed to Hadoop and the less the applications and reporting tools have to do.
Joins. Executing joins on big tables fast and efficiently is not always easy, especially if the SQL-on-Hadoop engine has no idea where the data is stored. An inefficient style of join processing can lead to massive amounts of I/O and can cause colossal data transport between nodes. Both can result in really poor performance.
Non-traditional data. Initially, SQL was designed to process highly structured data: Each record in a table has the same set of columns, and each column holds one atomic value. Not all big data in Hadoop has this traditional structure. Hadoop files may contain nested data, variable data (with hierarchical structures), schema-less data and self-describing data. A SQL-on-Hadoop engine must be able to translate all these forms of data to flat relational data, and must be able to optimize queries on these forms of data as well.
Storage format. Hadoop supports some “standard” storage formats of the data, such as Parquet, Avro and ORCFile. The more SQL-on-Hadoop technologies use such formats, the more tools and other SQL-on-Hadoop engines can read that same data. This drastically minimizes the need to replicate data. Thus, it’s important to verify whether a proprietary storage format is used.
User-defined functions. To use SQL to execute complex analytical functions, such as Gaussian discriminative analysis and market basket analysis, it’s important that they’re supported by SQL or can be developed. Such functions are called user-defined functions (UDFs). It’s also important that the SQL-on-Hadoop engine can distribute the execution of UDFs over as many nodes and disks as possible.
Multi-user workloads. It must be possible to set parameters that determine how the engine should divide its resources among different queries and different types of queries. For example, queries from different applications may have different processing priorities; long-running queries should get less priority than simple queries being processed concurrently; and unplanned and resource-intensive queries may have to be cancelled or temporarily interrupted if they use too many resources. SQL-on-Hadoop engines require smart and advanced workload managers.
Data federation. Not all data is stored in Hadoop. Most enterprise data is still stored in other data sources, such as SQL databases. A SQL-on-Hadoop engine must support distributed joins on data stored in all kinds of data sources. In other words, it must support data federation.
It would not surprise me if every organization that uses Hadoop eventually deploys a SQL-on-Hadoop engine (or maybe even a few). As organizations compare and evaluate the available technologies, assessing the engine’s capabilities for the requirements listed in this article is a great starting point.