Tag Archives: databases

Toward A Consistent, Fact-based Comparison of Emerging Database Technologies

A Muddle That Slows Adoption

Basho released RiakĀ as an open source project seven months ago and began commercial service shortly thereafter. As new entrants into the loose collection of database projects we observed two things:

  1. Widespread Confusion — the NoSQL rubric, and the decisions of most projects to self-identify under it, has created a false perception of overlap and similarity between projects differing not just technically but in approaches to licensing and distribution, leading to…
  2. Needless Competition — driving the confusion, many projects (us included, for sure) competed passionately (even acrimoniously) for attention as putative leaders of NoSQL, a fool’s errand as it turns out. One might as well claim leadership of all tools called wrenches.

The optimal use cases, architectures, and methods of software development differ so starkly even among superficially similar projects that to compete is to demonstrate a (likely pathological) lack of both user needs and self-knowledge.

This confusion and wasted energy — in other words, the market inefficiency — has been the fault of anyone who has laid claim to, or professed designs on, the NoSQL crown.

  1. Adoption suffers — Users either make decisions based on muddled information or, worse, do not make any decision whatsoever.
  2. Energy is wasted — At Basho we spent too much time from September to December answering the question posed without fail by investors and prospective users and clients: “Why will you ‘win’ NoSQL?”

With the vigor of fools, we answered this question, even though we rarely if ever encountered another project’s software in a “head-to-head” competition. (In fact, in the few cases where we have been pitted head-to-head against another project, we have won or lost so quickly that we cannot help but conclude the evaluation could have been avoided altogether.)

The investors and users merely behaved as rational (though often bemused) market actors. Having accepted the general claim that NoSQL was a monolithic category, both sought to make a bet.

Clearly what is needed is objective information presented in an environment of cooperation driven by mutual self-interest.

This information, shaped not by any one person’s necessarily imperfect understanding of the growing collection of data storage projects but rather by all the participants themselves, would go a long way to remedying the inefficiencies discussed above.

Demystification through data, not marketing claims

We have spoken to representatives of many of the emerging database projects. They have enthusiastically agreed to participate in a project to disclose data about each project. Disclosure will start with the following: a common benchmark framework and benchmarks/load tests modeling common use cases.

    1. A Common Benchmark Framework — For this collaboration to succeed, no single aspect will impact success or failure more than arriving at a common benchmark framework.

At Basho we have observed the proliferation of “microbenchmarks,” or benchmarks that do not reflect the conditions of a production environment. Benchmarks that use a small data set, that do not store to disk, that run for short (less than 12 hours) durations, do more to confuse the issue for end users than any single other factor. Participants will agree on benchmark methods, tools, applicability to use cases, and to make all benchmarks reproducible.

Compounding the confusion is when benchmarks are used for comparison of different use cases or was run on different hardware and yet compared head-to-head as if the tests or systems were identical. We will seek to help participants run equivalent on the various databases and we will not publish benchmark results that do not profile the hardware and configuration of the systems.

  1. Benchmarks That Support Use Cases — participants agree to benchmark their software under the conditions and with load tests reflective of use cases they commonly see in their user base or for which they think their software is best suited.
  2. Dissemination to third-parties — providing easy-to-find data to any party interested in posting results.
  3. Honestly exposing disagreement — Where agreement cannot be reached on any of the elements of the common benchmarking efforts, participants will respectfully expose the rationales for their divergent positions, thus still providing users with information upon which to base decisions.

There is more work to be done but all participants should begin to see the benefits: faster, better decisions by users.

We invite others to join, once we are underway. We, and our counterparts at other projects, believe this approach will go a long way to removing the inefficiencies hindering adoption of our software.

Tony and Justin