Not that kind of Shard

Tokutek steps up with new solution for MongoDB blues

Lucy Carey
Shard1

With TokuMX version 1.4, company is offering improved sharding that resolves the bottlenecks experienced by Mongo users.

 

Are MongoDB bottlenecks giving you grief?
John Partridge, CEO of database solution provider
Tokutek
, mighthave a solution for you. Here, speaks to JAXenter
about the company’s latest offering: TokuMX 1.4. This release
builds on the work the team have done to help deliver a highly
scalable database boosting solution with newly enhanced sharding,
which Tokutek claims will resolve performance issues in the biggest
NoSQL database around.

JAX: How did you identify the MongoDB ‘pain
points’ that you looked to address with this release?

Partridge: We read the comments
posted to various MongoDB discussion groups on Google Groups,
Reddit, Hacker News, etc. We also spoke at length with our existing
TokuDB customers who were also using MongoDB or contemplating doing
so.

Did you identify any other issues that your
team will be focusing on for
future releases?

Yes and they all revolve around improving MongoDB
performance for challenging Big Data workloads. They
relate to scaling out a MongoDB deployment, particularly with an
eye towards how MongoDB impacts DevOps at large
scale.

What were the biggest challenges faced in
developing TokuMX 1.4?

Frankly, the biggest issue was not to get too far
ahead of ourselves and risk a drop in robustness and
reliability. We wouldn’t want to introduce bugs in our desire to
rapidly bring new features and performance to
market.

Tokutek implements Fractal Tree indexes to offer high
performance on write-intensive workloads with the TokuDB database.
How did you arrive at the conclusion that this was the best
solution for data management?

The founders [of Tokutek] are academic researchers
from MIT, Rutgers, and SUNY Stony Brook with commercial experience
at companies like Google, Akamai, Thinking Machines, and Cray. When
they started digging into the problems that databases face when
handling Big Data workloads they quickly discovered that
indexing was the bottleneck.

In hindsight, that’s not a big surprise since the
traditional algorithm that Oracle, Microsoft, MySQL, MongoDB,
basically everybody, uses was developed in the 1970s.

It’s called the B-tree, and when the founder
discovered a better algorithm that can insert up to 200x (two
hundred; that’s not a typo) faster than a B-tree they knew it was
time to start a company.

Do you see MongoDB’s issues with scaling out
as potentially detrimental to the future of the database,
especially with so many rivals nipping at its heels?

MongoDB has done a terrific job on ease of use, both
at the language level and at the systems management
level. Tokutek adds enterprise class performance to that already
very attractive value proposition and we don’t
see any rival with a technology that can touch
the scaling capabilities of TokuMX.

What do you think the NoSQL market will be
like in the next few years? As convergence picks up pace within the
sector, what do you think the implications will be for
Tokutek?

As the NoSQL market matures I think we’ll see some
consolidation, especially as the developer community
establishes best practices and gains an understanding of
which tools are best for which types of
applications.

Tokutek stands to benefit under all scenarios because
what we do – advanced indexing – is essential for any workload
where the application needs to find a specific piece of information
more than once. Today, we’re uplifting MongoDB performance by
replacing its B-tree indexing with our Fractal Tree indexing.

If tomorrow a new product shows up on the scene that
is just as popular, the customers will want the performance and
cost savings that only our Fractal Tree indexing can deliver.

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