Clustering
Analysis of products and issues in database clustering. Relates subjects include:
CodeFutures/dbShards update
I’ve been talking a fair bit with Cory Isaacson, CEO of my client CodeFutures, which makes dbShards. Business notes include:
- 7 production users, plus an 8th imminent.
- 12-14 signed contracts beyond that.
- ~160 servers in production.
- One customer who has almost 15 terabytes of data (in the cloud).
- Still <10 people, pretty much all engineers.
- Profitable, but looking to raise a bit of growth capital.
Apparently, the figure of 6 dbShards customers in July, 2010 is more comparable to today’s 20ish contracts than to today’s 7-8 production users. About 4 of the original 6 are in production now.
NDA stuff aside, the main technical subject we talked about is something Cory calls “relational sharding”. The point is that dbShards’ transparent sharding can be done in such a way as to make many joins be single-server. Specifically:
- When a table is sufficiently small to be replicated in full at every nodes, you can join on it without moving data across the network.
- When two tables are sharded on the same key, you can join on that key without moving data across the network.
dbShards can’t do cross-shard joins, but it can do distributed transactions comprising multiple updates. Cory argues persuasively that in almost all cases this is enough; but I see cross-shard joins as a feature that should someday be added to dbShards even so.
The real issue with dbShards’ transparent sharding is ensuring it’s really transparent. Cory regards as typical a customer with a couple thousand tables, who had to change a dozen or so SQL statements to implement dbShards. But there are near-term plans to automate the number of SQL changes needed down to 0. The essence of that change is this: Read more
Categories: Clustering, Data integration and middleware, Data models and architecture, dbShards and CodeFutures, Market share and customer counts, Transparent sharding | 1 Comment |
DataStax Enterprise 2.0
Edit: Multiple errors in the post below have been corrected in a follow-on post about DataStax Enterprise and Cassandra.
My client DataStax is announcing DataStax Enterprise 2.0. The big point of the release is that there’s a bunch of stuff integrated together, including at least:
- Cassandra — the NoSQL DBMS, which DataStax sometimes calls “DataStax Server”. Edit: That’s not really a fair criticism of DataStax’s messaging.
- Hadoop MapReduce, which DataStax sometimes calls “Hadoop”. Edit: That is indeed fair. 🙂
- Sqoop — the general way to connect relational DBMS to Hadoop, which DataStax sometimes calls “RDBMS integration”.
- Solr — the search-centric Apache project, or big parts of it, which DataStax generally calls either “Solr” or “Solr compatibility”.
- log4j — an Apache project that has something or other to do with logging, or parts of it, which DataStax sometimes calls “log file integration”.
- DataStax OpsCenter — some management tools and so on around Cassandra and the rest of the product line.
DataStax stresses that all this runs on the same cluster, with the same administrative tools and so on. For example, on a single cluster:
- You can manage the interactive data for a web site.
- You can store the logs for that website.
- You can analyze all of the above in Hadoop.
Kinds of data integration and movement
“Data integration” can mean many different things, to an extent that’s impeding me from writing about the area. So I’ll start by simply laying out some of the myriad ways that data can be brought to where it is needed, and worry about other subjects later. Yes, this is a massive wall of text, and incomplete even so — but that in itself is my central point.
There are two main paradigms for data integration:
- Movement or replication — you take data from one place and copy it to another.
- Federation — you treat data in multiple different places logically as if it were all in one database.
Data movement and replication typically take one of three forms:
- Logical, transactional, or trigger-based — sending data across the wire every time an update happens, or as the result of a large-result-set query/extract, or in response to a specific request.
- Log-based — like logical replication, but driven by the transaction/update log rather than the core data management mechanism itself, so as to avoid directly overstressing the DBMS.
- Block/file-based — sending chunks of data, and expecting the target system to store them first and only make sense of them afterward.
Beyond the core functions of movement, replication, and/or federation, there are other concerns closely connected to data integration. These include:
- Transparency and emulation, e.g. via a layer of software that makes data in one format look like it’s in another. (If memory serves, this is the use case for which Larry DeBoever coined the term “middleware.”)
- Cleaning and quality — with new uses of data can come new requirements for accuracy.
- Master, reference, or canonical data —
- Archiving and information preservation — part of keeping data safe is ensuring that there are copies at various physical locations. Another part can be making it logically tamper-proof, or at least highly auditable.
In particular, the following are largely different from each other. Read more
Categories: Clustering, Data integration and middleware, EAI, EII, ETL, ELT, ETLT, eBay, Hadoop, MapReduce | 10 Comments |
Quick notes on MySQL Cluster
According to the MySQL Cluster home page, today’s MySQL Cluster release has — give or take terminology details 🙂 — added transparent sharding (Edit: Actually, please see the first comment below) and a memcached interface. My quick comments on all this to a reporter a couple of days ago were:
- Persistent memcached is a useful thing. Couchbase’s sales illustrate that point: http://www.dbms2.com/2012/02/01/couchbase-update/
- MySQL has always given good performance when used just as a key-value store, e.g. http://www.dbms2.com/2010/08/22/workday-technology-stack/ . So it’s reasonable to hope the memcached interface will have good performance out of the box.
- MySQL’s clustering capabilities have long been weak, providing a window of opportunity for companies and products such as Schooner Information and dbShards. The gold standard for clustering is:
- Efficient transparent sharding: http://www.dbms2.com/2011/02/24/transparent-sharding/
- Synchronous replication at much better than two-phase-commit speeds. http://www.dbms2.com/2011/10/23/schooner-pivots-further/
I don’t really know enough about MySQL Cluster right now to comment in more detail.
Categories: Clustering, memcached, MySQL, NoSQL, OLTP, Open source | 2 Comments |
MarkLogic’s Hadoop connector
It’s time to circle back to a subject I skipped when I otherwise wrote about MarkLogic 5: MarkLogic’s new Hadoop connector.
Most of what’s confusing about the MarkLogic Hadoop Connector lies in two pairs of options it presents you:
- Hadoop can talk XQuery to MarkLogic. But alternatively, Hadoop can use a long-established simple(r) Java API for streaming documents into or out of a MarkLogic database.
- Hadoop can make requests to MarkLogic in MarkLogic’s normal mode of operation, namely to address any node in the MarkLogic cluster, which then serves as a “head” node for the duration of that particular request. But alternatively, Hadoop can use a long-standing MarkLogic option to circumvent the whole DBMS cluster and only talk to one specific MarkLogic node.
Otherwise, the whole thing is just what you would think:
- Hadoop can read from and write to MarkLogic, in parallel at both ends.
- If Hadoop is just writing to MarkLogic, there’s a good chance the process is properly called “ETL.”
- If Hadoop is reading a lot from MarkLogic, there’s a good chance the process is properly called “batch analytics.”
MarkLogic said that it wrote this Hadoop connector itself.
Categories: Clustering, EAI, EII, ETL, ELT, ETLT, Hadoop, MapReduce, MarkLogic, Parallelization, Workload management | 2 Comments |
NoSQL notes
Last week I visited with James Phillips of Couchbase, Max Schireson and Eliot Horowitz of 10gen, and Todd Lipcon, Eric Sammer, and Omer Trajman of Cloudera. I guess it’s time for a round-up NoSQL post. 🙂
Views of the NoSQL market horse race are reasonably consistent, with perhaps some elements of “Where you stand depends upon where you sit.”
- As James tells it, NoSQL is simply a three-horse race between Couchbase, MongoDB, and Cassandra.
- Max would include HBase on the list.
- Further, Max pointed out that metrics such as job listings suggest MongoDB has the most development activity, and Couchbase/Membase/CouchDB perhaps have less.
- The Cloudera guys remarked on some serious HBase adopters.*
- Everybody I spoke with agreed that Riak had little current market presence, although some Basho guys could surely be found who’d disagree.
Categories: Basho and Riak, Cassandra, Cloudera, Clustering, Couchbase, HBase, Market share and customer counts, MongoDB, NoSQL, Open source, Oracle, Parallelization | 12 Comments |
Transparent relational OLTP scale-out
There’s a perception that, if you want (relatively) worry-free database scale-out, you need a non-relational/NoSQL strategy. That perception is false. In the analytic case it’s completely ridiculous, as has been demonstrated by Teradata, Vertica, Netezza, and various other MPP (Massively Parallel Processing) analytic DBMS vendors. And now it’s false for short-request/OLTP (OnLine Transaction Processing) use cases as well.
My favorite relational OLTP scale-out choice these days is the SchoonerSQL/dbShards partnership. Schooner Information Technology (SchoonerSQL) and Code Futures (dbShards) are young, small companies, but I’m not too concerned about that, because the APIs they want you to write to are just MySQL’s. The main scenarios in which I can see them failing are ones in which they are competitively leapfrogged, either by other small competitors – e.g. ScaleBase, Akiban, TokuDB, or ScaleDB — or by Oracle/MySQL itself. While that could suck for my clients Schooner and Code Futures, it would still provide users relying on MySQL scale-out with one or more good product alternatives.
Relying on non-MySQL NewSQL startups, by way of contrast, would leave me somewhat more concerned. (However, if their code is open sourced. you have at least some vendor-failure protection.) And big-vendor scale-out offerings, such as Oracle RAC or DB2 pureScale, may be more complex to deploy and administer than the MySQL and NewSQL alternatives.
Categories: Clustering, dbShards and CodeFutures, IBM and DB2, MySQL, NewSQL, NoSQL, OLTP, Open source, Oracle, Parallelization, Schooner Information Technology, Transparent sharding | 2 Comments |
Schooner pivots further
Schooner Information Technology started out as a complete-system MySQL appliance vendor. Then Schooner went software-only, but continued to brag about great performance in configurations with solid-state drives. Now Schooner has pivoted further, and is emphasizing high availability, clustered performance, and other hardware-agnostic OLTP (OnLine Transaction Processing) features. Fortunately, Schooner has some interesting stuff in those areas to talk about.
The short form of the SchoonerSQL (as Schooner’s product is now called) story goes roughly like this:
- SchoonerSQL replicates data — synchronously if the replication target is local, asynchronously if it is remote.
- Local synchronous replication provides high availability; remote asynchronous replication provides disaster recovery.
- SchoonerSQL’s local synchronous replication also provides read scale-out.
- Schooner has a partnership with Code Futures/dbShards to provide write scale-out via transparent sharding.
- SchoonerSQL has some secret sauce in replication performance. This has the effect of significantly increasing write performance (assuming you were going to replicate anyway), because otherwise you might have to slow down the master server’s write performance so that the slaves can keep up with it.
- Schooner believes it still has some single-server performance advantages as well.
Categories: Clustering, dbShards and CodeFutures, MySQL, OLTP, Oracle, Parallelization, Schooner Information Technology | 3 Comments |
Are there any remaining reasons to put new OLTP applications on disk?
Once again, I’m working with an OLTP SaaS vendor client on the architecture for their next-generation system. Parameters include:
- 100s of gigabytes of data at first, growing to >1 terabyte over time.
- High peak loads.
- Public cloud portability (but they have private data centers they can use today).
- Simple database design — not a lot of tables, not a lot of columns, not a lot of joins, and everything can be distributed on the same customer_ID key.
- Stream the data to a data warehouse, that will grow to a few terabytes. (Keeping only one year of OLTP data online actually makes sense in this application, but of course everything should go into the DW.)
So I’m leaning to saying: Read more
Couchbase technical update
My Couchbase business update with Bob Wiederhold was very interesting, but it didn’t answer much about the actual Couchbase product. For that, I talked with Dustin Sallings. We jumped around a lot, and some important parts of the Couchbase product haven’t had their designs locked down yet anyway. But here’s at least a partial explanation of what’s up.
memcached is a way to cache data in RAM across a cluster of servers and have it all look logically like a single memory pool, extremely popular among large internet companies. The Membase product — which is what Couchbase has been selling this year — adds persistence to memcached, an obvious improvement on requiring application developers to write both to memcached and to non-transparently-sharded MySQL. The main technical points in adding persistence seem to have been:
- A persistent backing store (duh), namely SQLite.
- A change to the hashing algorithm, to avoid losing data when the cluster configuration is changed.
Couchbase is essentially Membase improved by integrating CouchDB into it, with the main changes being:
- Changing the backing store to CouchDB (duh). This will be in the first Couchbase release.
- Adding cross data center replication on CouchDB’s consistency model. This will not, I believe, be in the first Couchbase release.
- Offering CouchDB’s programming and query interfaces as an option. So far as I can tell, this will be implemented straightforwardly in the first Couchbase release, with elegance planned for later down the road.
Let’s drill down a bit into Membase/Couchbase clustering and consistency. Read more