Having initially come to market with a straight-up operational database positioned for high-transactional performance, MemSQL is evolving to address the breadth and depth of enterprise data-processing requirements. The latest move sees the company embrace the Apache Spark in-memory analytics engine to enable real-time analysis alongside MemSQL's in-memory operational database and flash- or disk-based historical data store.

1. MemSQL connects with Apache Spark
for real-time in-memory analytics
Analyst: Matt Aslett
13 Feb, 2015
Having initially come to market with a straight-up operational database positioned for
high-transactional performance, MemSQL is evolving to address the breadth and depth of
enterprise data-processing requirements. The latest move sees the company embrace the Apache
Spark in-memory analytics engine to enable real-time analysis alongside MemSQL's in-memory
operational database and flash- or disk-based historical data store.
The 451 Take
MemSQL's connector for Apache Spark for high-performance real-time analytics could be seen
as to some extent validating the argument that it is necessary to have multiple
data-processing approaches to serve both transactional and analytic workloads. With the
previous addition of a columnar store, MemSQL enabled the storing and processing of
historical data for analytics. What Spark adds is the potential for in-memory analytic
processing alongside MemSQL, as well as access to libraries beyond SQL – such as streaming
and machine learning. Given they are both designed with a distributed in-memory
architecture, MemSQL and Spark should be a compelling combination for anyone exploring
their next-generation, high-performance data-processing requirements.
Context
MemSQL emerged in 2012 with an operational database positioned for high-performance
transactional applications thanks to its in-memory execution engine designed to convert SQL
statements into native C++ instructions. The company has expanded its purview since then. With
the launch of version 3.0 in April 2014, it added a column store to store and process historical data
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2. (on flash or spinning disk) and make MemSQL suitable for analytic, as well as transactional,
applications.
In addition, MemSQL had previously added support for the JSON data type in version 2.5 (late
2013), enabling it to support non-relational applications. The company has now added support for
real-time analytic processing by introducing a connector for the Apache Spark in-memory data
processing engine. Specifically, MemSQL introduced MemSQL Spark Connector, a free and open
source connector for the increasingly popular in-memory processing engine. The connector is
designed to take advantage of the distributed in-memory architectures of both MemSQL and Spark,
enabling parallel transfer of data between and the Spark RDD (resilient distributed dataset).
MemSQL sees a number of potential use cases for the MemSQL Spark Connector. Spark now has
access to both operational and historical data in MemSQL, while MemSQL is able to operationalize
models developed in Spark and take advantage of Spark's stream processing and machine-learning
capabilities. Users will be able to serve live dashboards from MemSQL while running more complex
real-time analytics workloads using Spark. Besides the technical integration enabled by the
MemSQL Spark Connector, MemSQL is also exploring the potential for a closer partnership with
commercial Apache Spark supporter Databricks, which was founded by the developers of Spark and
offers a cloud-based Spark offering, as well as working with software vendors to help their Spark
integration and support efforts.
MemSQL has grown steadily since our last update and now claims about 50 employees, compared
with 40 a year ago. It also says it has more than 40 paying customers (it previously claimed
'dozens'). We previously noted that the addition of reference customers would be a significant step
for the company if it wanted to convince mainstream adopters that it is capable of serving both
transactional and analytic workloads simultaneously. The increase to 40+ customers is therefore
significant, as is the growing list of names that are prepared to go on the record as MemSQL
customers (recent additions include digital media firm Ziff Davis and digital marketing firm
Kurtosys).
MemSQL raised $35m in January 2014 from Accel Partners, Khosla Ventures, Data Collective and
First Round Capital, bringing its total raised so far to $45m. It continues to be led by its
ex-Facebook founders. CEO Eric Frenkiel previously worked on partnership development and CTO
Nikita Shamgunov served as a software engineer at the social networking firm, while database
pioneer Jerry Held recently joined as executive chairman.
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3. Competition
The primary competition MemSQL is likely to face is the reliance on established incumbent
database providers such as Oracle, IBM and Microsoft (for general-purpose workloads), as well as
Teradata for analytics. While the former all offer databases that can be used to support
transactional or analytic workloads for performance reasons, it would be rare to find a company
running both simultaneously on the same database.
The assumption that it is necessary to deploy separate databases for analytic and transactional
workloads, and skepticism that it is possible to run both on the same database while maintaining
high performance for each, is also a major barrier to adoption for MemSQL as well as other
providers positioning for both – such as SAP with HANA, Deep Information Sciences with DeepDB,
JustOne Database and NuoDB.
MemSQL is most likely to be compared with HANA, thanks to its in-memory architecture, as well as
other in-memory providers such as VoltDB, Altibase and Pivotal. Given the widespread interest in
Apache Spark for in-memory analytics, we anticipate other vendors adding connectors. NoSQL
database provider DataStax has been the most active so far.
SWOT Analysis
Strengths Weaknesses
MemSQL offers a differentiated technology thanks
to its translation of SQL queries into native C++
instructions.
The company's plans are ambitious. Reference
customers continue to be key to convincing potential
adopters that it can deliver.
Opportunities Threats
In-memory databases are a hot topic, and Apache
Spark in particular is driving interest in new
approaches for in-memory data processing.
The incumbent relational database giants are making
memory-centric moves of their own, and will look to
crowd out emerging specialists.
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4. Reproduced by permission of The 451 Group; © 2015. This report was originally published within 451
Research's Market Insight Service. For additional information on 451 Research or to apply for trial access, go
to: www.451research.com
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