Theory and architecture
Analysis of design choices in databases and database management systems. Related subjects include:
- Any subcategory
- Database diversity
- Explicit support for specific data types
- (in Text Technologies) Text search
Three quick notes about derived data
I had one of “those” trips last week:
- 20 meetings, a number of them very multi-hour.
- A broken laptop.
- Flights that arrived 10:30ish Sunday night and left 7:00 Saturday morning.
So please pardon me if things are a bit disjointed …
I’ve argued for a while that:
- All human-generated data should be retained.
- The more important kinds of machine-generated data should be retained as well.
- Raw data isn’t enough; it’s really important to store derived data as well.
Here are a few notes on the derived data trend. Read more
| Categories: Derived data, Hadoop, Hortonworks, KXEN, Predictive modeling and advanced analytics | 8 Comments |
Many kinds of memory-centric data management
I’m frequently asked to generalize in some way about in-memory or memory-centric data management. I can start:
- The desire for human real-time interactive response naturally leads to keeping data in RAM.
- Many databases will be ever cheaper to put into RAM over time, thanks to Moore’s Law. (Most) traditional databases will eventually wind up in RAM.
- However, there will be exceptions, mainly on the machine-generated side. Where data creation and RAM data storage are getting cheaper at similar rates … well, the overall cost of RAM storage may not significantly decline.
Getting more specific than that is hard, however, because:
- The possibilities for in-memory data storage are as numerous and varied as those for disk.
- The individual technologies and products for in-memory storage are much less mature than those for disk.
- Solid-state options such as flash just confuse things further.
Consider, for example, some of the in-memory data management ideas kicking around. Read more
IBM DB2 10
Shortly before Tuesday’s launch of DB2 10, IBM’s Conor O’Mahony checked in for a relatively non-technical briefing.* More precisely, this is about DB2 for “distributed” systems, aka LUW (Linux/Unix/Windows); some of the features have already been in the mainframe version of DB2 for a while. IBM is graciously permitting me to post the associated DB2 10 announcement slide deck.
*I hope any errors in interpretation are minor.
Major aspects of DB2 10 include new or improved capabilities in the areas of:
- Compression.
- Analytic query performance.
- Data ingest.
- Multi-temperature data management.
- Workload management.
- Graph management/relationship analytics.
- Time-travel, bitemporal features, and bitemporal time-travel.
Of course, there are various other enhancements too, including to security (fine-grained access control), Oracle compatibility, and DB2 pureScale. Everything except the pureScale part is also reflected in IBM InfoSphere Warehouse, which is a near-superset of DB2.*
*Also, the data ingest part isn’t in base DB2.
| Categories: Data warehousing, Database compression, IBM and DB2, RDF and graphs, Solid-state memory, Workload management | 6 Comments |
DataStax Enterprise and Cassandra revisited
My last post about DataStax Enterprise and Cassandra didn’t go so well. As follow-up, I chatted for two hours with Rick Branson and Billy Bosworth of DataStax. Hopefully I can do better this time around.
For starters, let me say there are three kinds of data management nodes in DataStax Enterprise:
- Vanilla Cassandra.
- Cassandra plus Solr. Solr is a superset of the text-indexing system Lucene.
- Solr adds a lot more secondary indexing to Cassandra.
- In addition, these nodes serve as Solr emulation; you can run generic Solr apps on them.
- Cassandra plus Hadoop.
- You can use Hadoop MapReduce to manipulate generic Cassandra data.
- In addition, these nodes serve as Hadoop/HDFS (Hadoop Distributed File System) emulation; you can run generic Hadoop apps on them.
- Hadoop jobs can interweave access to the two kinds of data structure.
Cassandra, Solr, Lucene, and Hadoop are all Apache projects.
If we look at this from the standpoint of DML (Data Manipulation Language) and data access APIs:
- Cassandra is a column-group kind of NoSQL DBMS. You can get at its data programmatically.
- There’s something called CQL (Cassandra Query Language), said to be SQL-like.
- There’s a JDBC driver for CQL.
- With Hadoop MapReduce also come Hive, Pig, and Sqoop.
- With Solr and Lucene come full-text search.
In addition, it is sometimes recommended that you use “in-entity caching”, where an entire data structure (e.g. in JSON) winds up in a single Cassandra column.
The two main ways to get direct SQL* access to data in DataStax Enterprise are:
- JDBC/SQL.
- Hive/Hadoop.
*or very SQL-like, depending on how you view things
Before going further, let’s recall some Cassandra basics: Read more
| Categories: Cassandra, DataStax, Hadoop, MapReduce, Market share and customer counts, NoSQL, Open source, Text | 6 Comments |
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.
Akiban update
I have a bunch of backlogged post subjects in or around short-request processing, based on ongoing conversations with my clients at Akiban, Cloudant, Code Futures (dbShards), DataStax (Cassandra) and others. Let’s start with Akiban. When I posted about Akiban two years ago, it was reasonable to say:
- Akiban is in the short-request DBMS business.
- MySQL compatibility is one way to access Akiban, but it’s not the whole story.
- Akiban’s main point of technical differentiation is to arrange data hierarchically on disk so that many joins are “zero-cost”.
- Walking the hierarchy isn’t a great way to get at data for every possible query; Akiban recognizes the need for other access techniques as well.
All of the above are still true. But unsurprisingly, plenty of the supporting details have changed. Read more
| Categories: Akiban, Data models and architecture, MySQL, NewSQL, Object | 9 Comments |
Juggling analytic databases
I’d like to survey a few related ideas:
- Enterprises should each have a variety of different analytic data stores.
- Vendors — especially but not only IBM and Teradata — are acknowledging and marketing around the point that enterprises should each have a number of different analytic data stores.
- In addition to having multiple analytic data management technology stacks, it is also desirable to have an agile way to spin out multiple virtual or physical relational data marts using a single RDBMS. Vendors are addressing that need.
- Some observers think that the real essence of analytic data management will be in data integration, not the actual data management.
Here goes. Read more
Hardware and components — lessons from Teradata
I love talking with Carson Schmidt, chief of Teradata’s hardware engineering (among other things), even if I don’t always understand the details of what he’s talking about. It had been way too long since our last chat, so I requested another one. We were joined by Keith Muller, who I presume is pictured here. Takeaways included:
- Teradata performance growth was slow in the early 2000s, but has accelerated since then; Intel gets a lot of the credit (and blame) for that.
- Carson hopes for a performance “discontinuity” with Intel Ivy Bridge.
- Teradata is not afraid to use niche special-purpose chips.
- Teradata’s views can be taken as well-informed endorsements of InfiniBand and SAS 2.0.
| Categories: Data warehouse appliances, Data warehousing, Database compression, Solid-state memory, Storage, Teradata | 13 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 |