Clustrix Lifts the Curtain on Early Database Customers

Database startup Clustrix revealed the identities of four customers today, strong evidence that there’s something to its webscale SQL database beyond the $30 million investment that Clustrix has raised thus far. The customers announced are AOL, Photobox, Box.net and iOffer, and – according to the official press release, at least – all four agree that Clustrix’s status as a SQL database touting scalability helped spur the decision to give Clustrix a whirl. If these four are a microcosm of Clustrix’s business, or the market for SQL databases designed for massive scalability, we should only expect to see more growth.

The reality is that volumes are growing for all types of data, not just the unstructured data that might lead businesses to consider any of the emerging NoSQL or non-SQL database technologies. Organizations doing millions of transactions or otherwise producing relational data want products that meet their needs, too, and they want them to be minimally disruptive in terms of learning new technologies and rewriting applications. It’s hard to get less disruptive than keeping the database layer essentially the same.

Presently, most attempts to scale relational databases involves concepts like sharding or implementing a cache system, so Clustrix’s approach of scaling out the database itself by adding nodes is understandably attractive to customers. Thus far, the only other startup making noise around high-performance, scalable SQL is VoltDB, which focuses on online transaction processing. Just like NoSQL projects have proliferated to solve various problems related to unstructured data, Clustrix’s early success suggests that attempts to build scalable SQL databases might start ramping up, too.

An interesting side note to the Clustrix announcement is how it underscores the notion that SQL and NoSQL databases can coexist within the same organization. Clustrix customer AOL recently announced its work with NoSQL startup Membase (and Hadoop vendor Cloudera) around turning large volumes information on user events into a high-speed, targeted ad-serving system.