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Splice Machine

The concept behind it sounds like a dare: take the Hadoop NoSQL data store and use it to create a SQL relational database solution that can scale as easily as Hadoop. After a beta testing period that began in May of 2014, it’s available to integrate with traditional Hadoop jobs and backed by a support program that lets enterprises migrate existing workloads from Oracle, MySQL, and other traditional databases.

There is enhanced interest in a lambda-based systems. A database that is the open-source dual-engine RDBMS for mixed operational and analytical workloads, powered by Hadoop and Spark would prove to be very interesting.

Splice Machine
Splice Machine

This is what the Splice Machine claims to do. A RDBMS that executes operational workloads on Apache HBase® and analytical workloads on Apache Spark.

It says that it makes it easy to create modern, real-time, scaleable applications or to offload operational and analytical workloads from expensive Oracle, Teradata, and Netezza systems. The RDBMS has all of the key functionality of industrial SQL databases but on a scale-out architecture typically found in less functional NoSQL systems.

The Splice Machine RDBMS claims to support:

  • Optimized Joins
  • Secondary indexes
  • Aggregations
  • ACID Transactions
  • Concurrent small reads and writes (CRUD operations)
  • Stored Procedures
  • Triggers
  • Constraints
  • Window Functions

It claims to be like a Lambda Architecture in-a-box. They say that they make it very easy to use specialized compute engines for the right workload but do not require the developer to integrate those engines. Developers can use standard SQL to ingest, access, update, and analyze the database without worrying about what compute engine to use because the Splice Machine optimizer picks the right compute engine, already integrated, based on the nature of the query.

In the age of Big Data, companies need applications to provide the right results, right now, to users. Since thousands, if not millions, of people are reading and updating data simultaneously, high concurrency of small reads and writes are vital to act on this data in real time. Data warehouses, MPP databases and in-memory analytics databases do not have the ability to achieve high levels of concurrency, making them inadequate for operational applications.

When first introduced, Splice Machine faced a major obstacle to adoption: It wasn’t a drop-in replacement for existing RDBMS’s like Oracle, IBM DB2, Microsoft SQL Server, or MySQL. To get around that, Splice Machine is offering a support program called Safe Journey, which gives Splice Machine customers a migration path from their existing database solutions. It includes features like converting database stored procedures, which must be rewritten in Java to work in Splice Machine.

Splice Machine’s roots in a slew of open source projects open up the possibility that a competing Hadoop or RDBMS vendor will re-create Splice Machine’s innovations using the same open source bits. The results could then be relicensed more liberally since Splice Machine isn’t offered under an open source license.

Granted, Splice Machine has a free version of the product, but it’s offered only to companies “less than five years old and less than $10 million in revenues.”





[slideshare id=43592448&doc=architectureofsplicemachine-150116110747-conversion-gate02]–speed–says-splice-machine.html




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