Memory-centric data management
Analysis of technologies that manage data entirely or primarily in random-access memory (RAM). Related subjects include:
- Oracle TimesTen
- solidDB
- QlikTech
- SAP‘s BI Accelerator
- Exasol
- Solid-state memory as a replacement for disk
Real-time confusion
I recently proposed a 2×2 matrix of BI use cases:
- Is there an operational business process involved?
- Is there a focus on root cause analysis?
Let me now introduce another 2×2 matrix of analytic scenarios:
- Is there a compelling need for super-fresh data?
- Who’s consuming the results — humans or machines?
My point is that there are at least three different cool things people might think about when they want their analytics to be very fast:
- Fast investigative analytics — e.g., business intelligence with great query response.
- Computations on very fresh data, presented to humans — e.g. “heartbeat” graphics monitoring a network.
- Computations on very fresh data, presented back to a machine — e.g., a recommendation engine that includes makes good use of data about a user’s last few seconds of actions.
There’s also one slightly boring one that however drives a lot of important applications: Read more
| Categories: Business intelligence, Games and virtual worlds, Log analysis, Predictive modeling and advanced analytics, Splunk, Streaming and complex event processing (CEP), WibiData | 5 Comments |
Introduction to Platfora
When I wrote last week that I have at least 5 clients claiming they’re uniquely positioned to support BI over Hadoop (most of whom partner with a 6th client, Tableau) the non-partnering exception I had in mind was Platfora, Ben Werther’s oh-so-stealthy startup that is finally de-stealthing today. Platfora combines:
- An interesting approach to analytic data management.
- Business intelligence tools integrated with that.
The whole thing sounds like a perhaps more general and certainly non-SaaS version of what Metamarkets has been offering for a while.
The Platfora technical story starts: Read more
| Categories: Business intelligence, Columnar database management, Data models and architecture, Data warehousing, Database compression, Hadoop, Memory-centric data management, Platfora | 6 Comments |
Notes on the Oracle OpenWorld Sunday keynote
I’m not at Oracle OpenWorld, but as usual that won’t keep me from commenting. My bottom line on the first night’s announcements is:
- At many large enterprises, Oracle has a lock on much of their IT efforts. (But not necessarily in the internet or investigative analytics areas.) Tonight’s announcements serve to strengthen that.
- Tonight’s announcements do little to help Oracle in other market segments.
In particular:
1. At the highest level, my view of Oracle’s strategy is the same as it’s been for several years:
Clayton Christensen’s The Innovator’s Solution teaches us that Oracle should focus on selling a thick stack of technology to its highest-end customers, and that’s exactly what Oracle does focus on.
2. Tonight’s news is closely in line with what Oracle’s Juan Loaiza told me three years ago, especially:
- Oracle thinks flash memory is the most important hardware technology of the decade, one that could lead to Oracle being “bumped off” if they don’t get it right.
- Juan believes the “bulk” of Oracle’s business will move over to Exadata-like technology over the next 5-10 years. Numbers-wise, this seems to be based more on Exadata being a platform for consolidating an enterprise’s many Oracle databases than it is on Exadata running a few Especially Big Honking Database management tasks.
3. Oracle is confusing people with its comments on multi-tenancy. I suspect:
- What Oracle is talking about when it says “multi-tenancy” is more like consolidation than true multi-tenancy.
- Probably there are a couple of true multi-tenancy features as well.
4. SaaS (Software as a Service) vendors don’t want to use Oracle, because they don’t want to pay for it.* This limits the potential impact of Oracle’s true multi-tenancy features. Even so: Read more
When should analytics be in-memory?
I was asked today for rules or guidance regarding “analytical problems, situations, or techniques better suited for in-database versus in-memory processing”. There are actually two kinds of distinction to be drawn:
- Some workloads, in principle, should run on data to which there’s very fast and unfettered access — so fast and unfettered that you’d love the whole data set to be in RAM. For others, there is little such need.
- Some products, in practice, are coupled to specific in-memory data stores or to specific DBMS, even though other similar products don’t make the same storage assumptions.
Let’s focus on the first part of that — what work, in principle, should be done in memory? Read more
| Categories: Business intelligence, Data warehousing, Memory-centric data management, Parallelization, Predictive modeling and advanced analytics | 2 Comments |
Integrated internet system design
What are the central challenges in internet system design? We probably all have similar lists, comprising issues such as scale, scale-out, throughput, availability, security, programming ease, UI, or general cost-effectiveness. Screw those up, and you don’t have an internet business.
Much new technology addresses those challenges, with considerable success. But the success is usually one silo at a time — a short-request application here, an analytic database there. When it comes to integration, unsolved problems abound.
The top integration and integration-like challenges for me, from a practical standpoint, are:
- Integrating silos — a decades-old problem still with us in a big way.
- Dynamic schemas with joins.
- Low-latency business intelligence.
- Human real-time personalization.
Other concerns that get mentioned include:
- Geographical distribution due to privacy laws, which for some users is a major requirement for compliance.
- Logical data warehouse, a term that doesn’t actually mean anything real.
- In-memory data grids, which some day may no longer always be hand-coupled to the application and data stacks they accelerate.
Let’s skip those latter issues for now, focusing instead on the first four.
Aerospike, the former Citrusleaf
My new clients at Aerospike have a range of minor news to announce:
- A company and product name change (they used to be Citrusleaf).
- Some new people and funding.
- In association with an acqui-hire — of AlchemyDB guy Russ Sullivan — some unspecified future technical plans.
- A community edition (Aerospike, nee’ Citrusleaf, is closed-source).
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 has a key-value data model.
- Secondary indexes and so on are still futures.
- Aerospike is clustered, of course.
- Two hardware/storage choices are encouraged:
- Spinning disk, but you keep all your data in RAM.
- Solid-state disk.
AeroSpike’s three core marketing claims are performance, consistent performance, and uninterrupted operations.
- Aerospike’s performance claims are supported by a variety of blazing internal benchmarks.
- 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.
Aerospike technical details start with the expected: Read more
| Categories: Aerospike, Market share and customer counts, Memory-centric data management, NoSQL, Pricing | 2 Comments |
In-memory, (hybrid) memory-centric DBMS — three analytic glossary draft entries
These are three closely-related draft entries for the DBMS2 analytic glossary. Please comment with any ideas you have for their improvement!
1. We coined the term memory-centric data management to comprise several kinds of technology that manage data in RAM (Random Access Memory), including:
- In-memory DBMS (DataBase Management Systems).
- Hybrid memory-centric DBMS.
- Other kinds of in-memory data stores, such as:
- Caching layers.
- In-memory data stores that are tightly tied to specific analytic tools, for example the in-memory data management part of QlikView.
- Complex event/stream processing.
Related link
- Many examples of memory-centric data management (April, 2012)
2. An in-memory DBMS is a DBMS designed under the assumption that substantially all database operations will be performed in RAM (Random Access Memory). Thus, in-memory DBMS form a subcategory of memory-centric data management systems.
Ways in which in-memory DBMS are commonly different from those that query and update persistent storage include: Read more
| Categories: Analytic glossary, Cache, In-memory DBMS, Memory-centric data management, Streaming and complex event processing (CEP) | 7 Comments |
Notes, links and comments August 6, 2012
I haven’t done a notes/link/comments post for a while. Time for a little catch-up.
1. MySQL now has a memcached integration story. I haven’t checked the details. The MySQL team is pretty hard to talk with, due to the heavy-handedness of Oracle’s analyst relations.
2. The Large Hadron Collider offers some serious numbers, including:
- 1 petabyte/second.
- 6 x 109 collisions/second.
- Only 1 in 1013 collision records kept (which I guess knocks things down to a 100 byte/second average, from the standpoint of persistent storage).
- Real-time filtering by a cluster of several thousand machines, over a 25 nanosecond period.
3. One application area we don’t talk about much for analytic technologies is education. However: Read more
| Categories: Cache, memcached, Memory-centric data management, MySQL, Open source, Petabyte-scale data management, RDF and graphs, Scientific research | Leave a Comment |
Why I recommend avoiding Kognitio
Since my recent post about Kognitio, things have gotten worse. The company is insistently pushing the marketing message that Kognitio has always been an in-memory product, and at one point went so far as to publicly pretend that I had agreed.
I do not agree. Yes, it’s fair to say — as I did in 2008 — that Kognitio is very RAM-centric, but that’s not at all the same thing. In particular:
- I did due diligence for Warburg Pincus’ original investment in Kognitio in the 1990s (it was then called White Cross). I have no memory of an in-memory positioning, nor of discussing same with anybody.
- I checked my notes from a 2006 briefing, which included Kognitio CTO Roger Gaskell. There was no claim that Kognitio was an in-memory product.
- Indeed, as I also posted in 2008, Kognitio keeps indexes on disk. If you use indexes on disk, you’re not an in-memory product.
The truth is that Kognitio offers a disk-based DBMS that has long been worked on by a small team. I believe that the team really has put considerable effort into how Kognitio uses RAM. But there’s no basis to give Kognitio credit for being “really” in-memory vs. a variety of other analytic RDBMS alternatives. And a row-based product that doesn’t currently offer compression is at a large disadvantage versus, say, columnar products that already do.*
*Columnar systems don’t clobber row-based ones in-memory as extremely as they do in some disk-based use cases. But even in-memory it’s good not to have to move around data that isn’t relevant to your query.
Until Kognitio gets at least somewhat more honest in its marketing, I recommend avoiding Kognitio like the plague. It’s simply not a big enough company to buy from unless you have some level of trust in the management team.
| Categories: Columnar database management, Database compression, In-memory DBMS, Kognitio, Memory-centric data management | 1 Comment |
Memory-centric data management when locality matters
Ron Pressler of Parallel Universe/SpaceBase pinged me about a data grid product he was open sourcing, called Galaxy. The idea is that a distributed RAM grid will allocate data, not randomly or via consistent hashing, but rather via a locality-sensitive approach. Notes include:
- The original technology was developed to track moving objects on behalf of the Israeli Air Force.
- The commercial product is focused on MMO (Massively MultiPlayer Online) games (or virtual worlds).
- The underpinnings are being open sourced.
- Ron suggests that, among other use cases, Galaxy might work well for graphs.
- Ron argues that one benefit is that when lots of things cluster together — e.g. characters in a game — there’s a natural way to split them elastically (shrink the radius for proximity).
- The design philosophy seems to be to adapt as many ideas as possible from the way CPUs manage (multiple levels of) RAM cache.
The whole thing is discussed in considerable detail in a blog post and a especially in a Hacker News comment thread. There’s also an error-riddled TechCrunch article. Read more