Analysis of data warehouse DBMS vendor Aster Data. Related subjects include:
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:
- The object/relational, aka universal, extensibility features developed in the 1990s for Oracle, DB2, Informix, Illustra, and Postgres. The most successful extensions probably have been:
- Geospatial indexing via ESRI.
- Full-text indexing, notwithstanding questionable features and performance.
- MySQL storage engines.
- MPP (Massively Parallel Processing) analytic RDBMS relying on single-node PostgreSQL, Ingres, and/or Microsoft SQL Server — e.g. Greenplum (especially early on), Aster (ditto), DATAllegro, DATAllegro’s offspring Microsoft PDW (Parallel Data Warehouse), or Hadapt.
- Splits in which a DBMS has serious processing both in a “database” layer and in a predicate-pushdown “storage” layer — most famously Oracle Exadata, but also MarkLogic, InfiniDB, and others.
- SQL-on-HDFS — Hive, Impala, Stinger, Shark and so on (including Hadapt).
Other examples on my mind include:
- Data manipulation APIs being added to key-value stores such as Couchbase and Aerospike.
- TokuMX, the Tokutek/MongoDB hybrid I just blogged about.
- NuoDB’s willing reliance on third-party key-value stores (or HDFS in the role of one).
- FoundationDB’s strategy, and specifically its acquisition of Akiban.
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
The genesis of this post is:
- Dave DeWitt sent me a paper about Microsoft Polybase.
- I argued with Dave about the differences between Polybase and Hadapt.
- I asked Daniel Abadi for his opinion.
- Dan agreed with Dave, in a long email …
- … that he graciously permitted me to lightly-edit and post.
I love my life.
Per Daniel (emphasis mine): Read more
|Categories: Aster Data, Data warehousing, Greenplum, Hadapt, Hadoop, MapReduce, Microsoft and SQL*Server, SQL/Hadoop integration, Theory and architecture||13 Comments|
The cardinal rules of DBMS development
Rule 1: Developing a good DBMS requires 5-7 years and tens of millions of dollars.
That’s if things go extremely well.
Rule 2: You aren’t an exception to Rule 1.
- Concurrent workloads benchmarked in the lab are poor predictors of concurrent performance in real life.
- Mixed workload management is harder than you’re assuming it is.
- Those minor edge cases in which your Version 1 product works poorly aren’t minor after all.
DBMS with Hadoop underpinnings …
… aren’t exceptions to the cardinal rules of DBMS development. That applies to Impala (Cloudera), Stinger (Hortonworks), and Hadapt, among others. Fortunately, the relevant vendors seem to be well aware of this fact. Read more
I’ve hacked both the PHP and CSS that drive this website. But if I had to write PHP or CSS from scratch, I literally wouldn’t know how to begin.
Something similar, I suspect, is broadly true of “business analysts.” I don’t know how somebody can be a competent business analyst without being able to generate, read, and edit SQL. (Or some comparable language; e.g., there surely are business analysts who only know MDX.) I would hope they could write basic SELECT statements as well.
But does that mean business analysts are comfortable with the fancy-schmantzy extended SQL that the analytic platform vendors offer them? I would assume that many are but many others are not. And thus I advised such a vendor recently to offer sample code, and lots of it — dozens or hundreds of isolated SQL statements, each of which does a specific task.* A business analyst could reasonably be expected to edit any of those to point them his own actual databases, even though he can’t necessarily be expected to easily write such statements from scratch. Read more
I took the opportunity of Teradata’s Aster/Hadoop appliance announcement to catch up with Teradata hardware chief Carson Schmidt. I love talking with Carson, about both general design philosophy and his views on specific hardware component technologies.
From a hardware-requirements standpoint, Carson seems to view Aster and Hadoop as more similar to each other than either is to, say, a Teradata Active Data Warehouse. In particular, for Aster and Hadoop:
- I/O is more sequential.
- The CPU:I/O ratio is higher.
- Uptime is a little less crucial.
The most obvious implication is differences in the choice of parts, and of their ratio. Also, in the new Aster/Hadoop appliance, Carson is content to skate by with RAID 5 rather than RAID 1.
I think Carson’s views about flash memory can be reasonably summarized as: Read more
|Categories: Aster Data, Data warehouse appliances, Data warehousing, Hadoop, Solid-state memory, Storage, Teradata||2 Comments|
Two of the more interesting approaches for integrating Hadoop and MapReduce with relational DBMS come from my clients at Teradata Aster (via SQL/MR and SQL-H) and Hadapt. In both cases, the story starts:
- You can dump any kind of data you want into Hadoop’s file system.
- You can have data in a scale-out RDBMS to get good performance on analytic SQL.
- You can access all the data (not just the relationally stored part) via SQL.
- You can do MapReduce on all the data (not just the Hadoop-stored part).
- To varying degrees, Hadapt and Aster each offer three kinds of advantage over Hadoop-with-Hive:
- SQL performance is (much) better.
- SQL functionality is better.
- At least some of your employees — the “business analysts” — can invoke MapReduce processes through SQL, if somebody else (e.g. your techies or the vendor’s) coded them up in the first place.
Of course, there are plenty of differences. Those start: Read more
My clients at Teradata are introducing a mix-em/match-em Aster/Hadoop box, officially called the Teradata Aster Big Analytics Appliance. Basics include:
- You can fill a rack with nodes either for the Aster DBMS or for Hadoop (Hortonworks flavor), or you can combine them in the same box.
- If you combine them, they share management software (adapted from mainstream Teradata’s) and Infiniband.
- An Aster node has 16 2.6-gigahertz cores and 24 900GB disk drives.
- A Hadoop node has 12 2.0-gigahertz cores and 12 3TB drives.
- A central part of Teradata’s strategy is that Aster and Hadoop nodes can work together via SQL-H.
- The Teradata Aster Big Analytics Appliance is based on a family of Dell servers that fit more compactly into racks than do Teradata’s traditional products.
- The Teradata Aster Big Analytics Appliance replaces a previous interim Teradata Aster appliance that used similar hardware to that in other Teradata systems.
My views on the Teradata Aster Big Analytics Appliance start: Read more
I was asked to clarify one of my July comments on Oracle12c,
I wonder whether Oracle will finally introduce a true columnar storage option, a year behind Teradata. That would be the obvious enhancement on the data warehousing side, if they can pull it off. If they can’t, it’s a damning commentary on the core Oracle codebase.
by somebody smart who however seemed to have half-forgotten my post comparing (hybrid) columnar compression to (hybrid) columnar storage.
In simplest terms:
- Columnar storage and columnar compression are two different things. The main connections are:
- Columnar storage can make columnar compression more effective.
- In different ways, both technologies reduce I/O.
- EMC Greenplum, Teradata Aster, and Teradata Classic are all originally row-based systems that have gone hybrid columnar.
- Vertica is an originally column-based system that has gone hybrid columnar.
|Categories: Aster Data, Columnar database management, Data warehousing, Database compression, Greenplum, Oracle, Teradata, Vertica Systems||4 Comments|
This post started as a minor paragraph in another one I’m drafting. But it grew. Please also see the comment thread below.
Increasingly many data management systems store data in a cluster, putting several copies of data — i.e. “replicas” — onto different nodes, for safety and reliable accessibility. (The number of copies is called the “replication factor”.) But how do they know that the different copies of the data really have the same values? It seems there are three main approaches to immediate consistency, which may be called:
- Two-phase commit (2PC)
- Read-your-writes (RYW) consistency
- Prudent optimism
I shall explain.
Two-phase commit has been around for decades. Its core idea is:
- One node commands other nodes (and perhaps itself) to write data.
- The other nodes all reply “Aye, aye; we are ready and able to do that.”
- The first node broadcasts “Make it so!”
Unless a piece of the system malfunctions at exactly the wrong time, you’ll get your consistent write. And if there indeed is an unfortunate glitch — well, that’s what recovery is for.
But 2PC has a flaw: If a node is inaccessible or down, then the write is blocked, even if other parts of the system were able to accept the data safely. So the NoSQL world sometimes chooses RYW consistency, which in essence is a loose form of 2PC: Read more
|Categories: Aster Data, Clustering, Hadoop, HBase, IBM and DB2, Netezza, NoSQL, Teradata, Vertica Systems||10 Comments|
This is a draft entry for the DBMS2 analytic glossary. Please comment with any ideas you have for its improvement!
Note: Words and phrases in italics will be linked to other entries when the glossary is complete.
“In-database analytics” is a catch-all term for analytic capabilities, beyond standard SQL, running on the same machine as and under the management of an analytic DBMS. These can run in one or both of two modes:
- In-process or unfenced, i.e. in the same process as the DBMS itself. This option gives maximum performance, but any defects in the analytic code may crash the whole DBMS. Also, it generally requires that the code be in the same language as the DBMS, i.e. C++.
- Out-of-process or fenced, i.e. in a separate process. This option sacrifices performance, in favor of reliability and language flexibility.
In-database analytics may offer great performance and scalability advantages versus the alternative of extracting data and having it be processed on a separate server. This is particularly likely to be the case in MPP (Massively Parallel Processing) analytic DBMS environments.
Examples of in-database analytics include:
- Creating temporary data structures that persist past the life of a query.
- Creating temporary data structures that are non-tabular.
- Predictive modeling that uses all the same nodes in an MPP cluster where the data resides.
- Predictive analytics (scoring only).
Other common domains for in-database analytics include sessionization, time series analysis, and relationship analytics.
Notable products offering in-database analytics include:
- Teradata Aster SQL/MR.
- Multiple other analytic platforms, such as Sybase IQ, Vertica, or IBM Netezza. Indeed, in-database analytics are a defining feature of analytic platforms.
- Fuzzy Logix (for predictive analytics).
|Categories: Analytic glossary, Aster Data, Data warehousing, IBM and DB2, MapReduce, Netezza, Parallelization, Predictive modeling and advanced analytics, Sybase, Teradata, Vertica Systems||8 Comments|