Aerospike

Discussion of AeroSpike, formerly known as Citrusleaf.

January 3, 2014

Notes on memory-centric data management

I first wrote about in-memory data management a decade ago. But I long declined to use that term — because there’s almost always a persistence story outside of RAM — and coined “memory-centric” as an alternative. Then I relented 1 1/2 years ago, and defined in-memory DBMS as

DBMS designed under the assumption that substantially all database operations will be performed in RAM (Random Access Memory)

By way of contrast:

Hybrid memory-centric DBMS is our term for a DBMS that has two modes:

  • In-memory.
  • Querying and updating (or loading into) persistent storage.

These definitions, while a bit rough, seem to fit most cases. One awkward exception is Aerospike, which assumes semiconductor memory, but is happy to persist onto flash (just not spinning disk). Another is Kognitio, which is definitely lying when it claims its product was in-memory all along, but may or may not have redesigned its technology over the decades to have become more purely in-memory. (But if they have, what happened to all the previous disk-based users??)

Two other sources of confusion are:

With all that said, here’s a little update on in-memory data management and related subjects.

And finally,

November 8, 2013

Comments on the 2013 Gartner Magic Quadrant for Operational Database Management Systems

The 2013 Gartner Magic Quadrant for Operational Database Management Systems is out. “Operational” seems to be Gartner’s term for what I call short-request, in each case the point being that OLTP (OnLine Transaction Processing) is a dubious term when systems omit strict consistency, and when even strictly consistent systems may lack full transactional semantics. As is usually the case with Gartner Magic Quadrants:

Anyhow:  Read more

September 8, 2013

Layering of database technology & DBMS with multiple DMLs

Two subjects in one post, because they were too hard to separate from each other

Any sufficiently complex software is developed in modules and subsystems. DBMS are no exception; the core trinity of parser, optimizer/planner, and execution engine merely starts the discussion. But increasingly, database technology is layered in a more fundamental way as well, to the extent that different parts of what would seem to be an integrated DBMS can sometimes be developed by separate vendors.

Major examples of this trend — where by “major” I mean “spanning a lot of different vendors or projects” — include:

Other examples on my mind include:

And there are several others I hope to blog about soon, e.g. current-day PostgreSQL.

In an overlapping trend, DBMS increasingly have multiple data manipulation APIs. Examples include:  Read more

August 17, 2013

Aerospike 3

My clients at Aerospike are coming out with their Version 3, and as several of my clients do, have encouraged me to front-run what otherwise would be the Monday embargo.

I encourage such behavior with arguments including:

Aerospike 2’s value proposition, let us recall, was:

… performance, consistent performance, and uninterrupted operations …

  • Aerospike’s consistent performance claims are along the lines of sub-millisecond latency, with 99.9% of responses being within 5 milliseconds, and even a node outage only borking performance for some 10s of milliseconds.
  • Uninterrupted operation is a core Aerospike design goal, and the company says that to date, no Aerospike production cluster has ever gone down.

The major support for such claims is Aerospike’s success in selling to the digital advertising market, which is probably second only to high-frequency trading in its low-latency demands. For example, Aerospike’s CMO Monica Pal sent along a link to what apparently is:

Read more

April 25, 2013

Analytic application themes

I talk with a lot of companies, and repeatedly hear some of the same application themes. This post is my attempt to collect some of those ideas in one place.

1. So far, the buzzword of the year is “real-time analytics”, generally with “operational” or “big data” included as well. I hear variants of that positioning from NewSQL vendors (e.g. MemSQL), NoSQL vendors (e.g. AeroSpike), BI stack vendors (e.g. Platfora), application-stack vendors (e.g. WibiData), log analysis vendors (led by Splunk), data management vendors (e.g. Cloudera), and of course the CEP industry.

Yeah, yeah, I know — not all the named companies are in exactly the right market category. But that’s hard to avoid.

Why this gold rush? On the demand side, there’s a real or imagined need for speed. On the supply side, I’d say:

2. More generally, most of the applications I hear about are analytic, or have a strong analytic aspect. The three biggest areas — and these overlap — are:

Also arising fairly frequently are:

I’m hearing less about quality, defect tracking, and equipment maintenance than I used to, but those application areas have anyway been ebbing and flowing for decades.

Read more

January 17, 2013

YCSB benchmark notes

Two different vendors recently tried to inflict benchmarks on me. Both were YCSBs, so I decided to look up what the YCSB (Yahoo! Cloud Serving Benchmark) actually is. It turns out that the YCSB:

That actually sounds pretty good, especially the extensibility part;* it’s likely that the YCSB can be useful in a variety of product selection scenarios. Still, as recent examples show, benchmark marketing is an annoying blight upon the database industry.

*With extensibility you can test your own workloads and do your own sensitivity analyses.

A YCSB overview page features links both to the code and to the original explanatory paper. The clearest explanation of the YCSB I found there was: Read more

August 27, 2012

Aerospike, the former Citrusleaf

My new clients at Aerospike have a range of minor news to announce:

Mainly, however, they want to call your attention to the fact that they’ve been selling a fast, reliable key-value store, with a number of production references, and want to suggest that other organizations should perhaps buy it as well.

Generally, the Aerospike product story is as I described in two posts last year. At the highest level:

AeroSpike’s three core marketing claims are performance, consistent performance, and uninterrupted operations.

Aerospike technical details start with the expected: Read more

June 22, 2011

Citrusleaf RTA

Citrusleaf has released an add-on product called Citrusleaf RTA (Real-Time Attribution). It’s to be used when:

The metrics envisioned are:

A consistent relational schema is NOT assumed.

Citrusleaf’s solution is:

The downside is that when you do read 100 objects/records per person, you might need to do 100 seeks.

March 29, 2011

Introduction to Citrusleaf

Citrusleaf is the vendor of yet another short-request/NoSQL database management system, conveniently named Citrusleaf. Highlights for Citrusleaf the company include:

Citrusleaf the product is a kind of key-value store; however, the values are in the form of rows, so what you really look up is (key, field name, value) triples. Right now only the keys are indexed; futures include indexing on the individual fields, so as to support some basic analytics. SQL support is an eventual goal. Other Citrusleaf buzzword basics include:

To date, Citrusleaf customers have focused on sub-millisecond data retrieval, preferably .2-.3 milliseconds. Accordingly, none has chosen to put the primary Citrusleaf data store on disk. Rather:

I don’t have a good grasp on what the data structure for those indexes is.

Citrusleaf characterizes its customers as firms that have “a couple of KB” of data on “every” person in North America. Naively, that sounds like a terabyte or less to me, but Citrusleaf says 1-3 terabytes is most common. Or to quote the press release, “The most common deployments for Citrusleaf 2.0 are terabytes of data, billions of objects, and 200K plus transactions per second per node, with sub-millisecond latency.” 4-8 nodes seems to be typical for Citrusleaf databases (all figures pre-replication). I didn’t ask what kind of hardware is at each node.

Citrusleaf data distribution features include:  Read more

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