Discussion of choices and variety in database management system architecture. Related subjects include:
I talked with a couple of Cloudera folks about HBase last week. Let me frame things by saying:
- The closest thing to an HBase company, ala MongoDB/MongoDB or DataStax/Cassandra, is Cloudera.
- Cloudera still uses a figure of 20% of its customers being HBase-centric.
- HBaseCon and so on notwithstanding, that figure isn’t really reflected in Cloudera’s marketing efforts. Cloudera’s marketing commitment to HBase has never risen to nearly the level of MongoDB’s or DataStax’s push behind their respective core products.
- With Cloudera’s move to “zero/one/many” pricing, Cloudera salespeople have little incentive to push HBase hard to accounts other than HBase-first buyers.
- Cloudera no longer dominates HBase development, if it ever did.
- Cloudera is the single biggest contributor to HBase, by its count, but doesn’t make a majority of the contributions on its own.
- Cloudera sees Hortonworks as having become a strong HBase contributor.
- Intel is also a strong contributor, as are end user organizations such as Chinese telcos. Not coincidentally, Intel was a major Hadoop provider in China before the Intel/Cloudera deal.
- As far as Cloudera is concerned, HBase is just one data storage technology of several, focused on high-volume, high-concurrency, low-latency short-request processing. Cloudera thinks this is OK because of HBase’s strong integration with the rest of the Hadoop stack.
- Others who may be inclined to disagree are in several cases doing projects on top of HBase to extend its reach. (In particular, please see the discussion below about Apache Phoenix and Trafodion, both of which want to offer relational-like functionality.)
|Categories: Cloudera, Clustering, Data models and architecture, Database diversity, Hadoop, HBase, Hortonworks, HP and Neoview, Intel, Market share and customer counts, NoSQL, Open source||4 Comments|
7-10 years ago, I repeatedly argued the viewpoints:
- Relational DBMS were the right choice in most cases.
- Multiple kinds of relational DBMS were needed, optimized for different kinds of use case.
- There were a variety of specialized use cases in which non-relational data models were best.
Since then, however:
- Hadoop has flourished.
- NoSQL has flourished.
- Graph DBMS have matured somewhat.
- Much of the action has shifted to machine-generated data, of which there are many kinds.
So it’s probably best to revisit all that in a somewhat organized way.
I’m commonly asked to assess vendor claims of the kind:
- “Our system lets you do multiple kinds of processing against one database.”
- “Otherwise you’d need two or more data managers to get the job done, which would be a catastrophe of unthinkable proportion.”
So I thought it might be useful to quickly review some of the many ways organizations put multiple data stores to work. As usual, my bottom line is:
- The most extreme vendor marketing claims are false.
- There are many different choices that make sense in at least some use cases each.
Horses for courses
It’s now widely accepted that different data managers are better for different use cases, based on distinctions such as:
- Short-request vs. analytic.
- SQL vs. non-SQL (NoSQL or otherwise).
- Expensive/heavy-duty vs. cheap/easy-to-support.
Vendors are part of this consensus; already in 2005 I observed
For all practical purposes, there are no DBMS vendors left advocating single-server strategies.
Vendor agreement has become even stronger in the interim, as evidenced by Oracle/MySQL, IBM/Netezza, Oracle’s NoSQL dabblings, and various companies’ Hadoop offerings.
Multiple data stores for a single application
We commonly think of one data manager managing one or more databases, each in support of one or more applications. But the other way around works too; it’s normal for a single application to invoke multiple data stores. Indeed, all but the strictest relational bigots would likely agree: Read more
The name of this blog comes from an August, 2005 column. 8 1/2 years later, that analysis holds up pretty well. Indeed, I’d keep the first two precepts exactly as I proposed back then:
- Task-appropriate data managers. Much of this blog is about task-appropriate data stores, so I won’t say more about them in this post.
- Drastic limitations on relational schema complexity. I think I’ve been vindicated on that one by, for example:
- NoSQL and dynamic schemas.
- Schema-on-read, and its smarter younger brother schema-on-need.
- Limitations on the performance and/or allowed functionality of joins in scale-out short-request RDBMS, and the relative lack of complaints about same.
- Funky database design from major Software as a Service (SaaS) vendors such as Workday and Salesforce.com.
- A whole lot of logs.
I’d also keep the general sense of the third precept, namely appropriately-capable data integration, but for that one the specifics do need some serious rework.
For starters, let me say: Read more
|Categories: About this blog, Business intelligence, Database diversity, EAI, EII, ETL, ELT, ETLT, Investment research and trading, NoSQL, Schema on need||2 Comments|
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
Perhaps the single toughest question in all database technology is: Which different purposes can a single data store serve well? — or to phrase it more technically — Which different usage patterns can a single data store support efficiently? Ted Codd was on multiple sides of that issue, first suggesting that relational DBMS could do everything and then averring they could not. Mike Stonebraker too has been on multiple sides, first introducing universal DBMS attempts with Postgres and Illustra/Informix, then more recently suggesting the world needs 9 or so kinds of database technology. As for me — well, I agreed with Mike both times.
Since this is MUCH too big a subject for a single blog post, what I’ll do in this one is simply race through some background material. To a first approximation, this whole discussion is mainly about data layouts — but only if we interpret that concept broadly enough to comprise:
- Every level of storage (disk, RAM, etc.).
- Indexes, aggregates and raw data alike.
To date, nobody has ever discovered a data layout that is efficient for all usage patterns. As a general rule, simpler data layouts are often faster to write, while fancier ones can boost query performance. Specific tradeoffs include, but hardly are limited to: Read more
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.
From time to time, I try to step back and build a little taxonomy for the variety in database technology. One effort was 4 1/2 years ago, in a pre-planned exchange with Mike Stonebraker (his side, alas, has since been taken down). A year ago I spelled out eight kinds of analytic database.
The angle I’ll take this time is to say that every sufficiently large enterprise needs to be cognizant of at least 7 kinds of database challenge. General notes on that include:
- I’m using the weasel words “database challenge” to evade questions as to what is or isn’t exactly a DBMS.
- One “challenge” can call for multiple products and technologies even within a single enterprise, let alone at different ones. For example, in this post the “eight kinds of analytic database” are reduced to just a single category.
- Even so, one product or technology may be well-suited to address a couple different kinds of challenges.
The Big Seven database challenges that almost any enterprise faces are: Read more
|Categories: Data integration and middleware, Data models and architecture, Database diversity, EAI, EII, ETL, ELT, ETLT, Hadoop, Memory-centric data management, NoSQL, Object, OLTP, RDF and graphs, Structured documents, Talend, Text||3 Comments|
In August 2010, I wrote about Workday’s interesting technical architecture, highlights of which included:
- Lots of small Java objects in memory.
- A very simple MySQL backing store (append-only, <10 tables).
- Some modernistic approaches to application navigation.
- A faceted approach to BI.
I caught up with Workday recently, and things have naturally evolved. Most of what we talked about (by my choice) dealt with data management, business intelligence, and the overlap between the two.
It is now reasonable to say that Workday’s servers fall into at least seven tiers, although we talked mainly about five that work together as a kind of giant app/database server amalgamation. The three that do noteworthy data management can be described as:
- In-memory objects and transactions. This is similar to what Workday had before.
- Persistent MySQL. Part of this is similar to what Workday had before. In addition, Workday is now storing certain data in tables in the ordinary relational way.
- In-memory caching and indexing. This has three aspects:
- Indexes for the ordinary relational tables, organized in interesting ways.
- Indexes for Workday’s search-box navigation (as per my original Workday technical post, you can search across objects, task-names, etc.).
- Compressed copies of the Java objects, used to instantiate other servers as needed. The most obvious uses of this are:
- Recovery for the object/transaction tier.
- Launch for the elastic compute tier. (Described below.)
Two other Workday server tiers may be described as: Read more
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