Analysis of data management technology based on a structured-document model, or optimized for XML data. Related subjects include:
I talked with the Snowflake Computing guys Friday. For starters:
- Snowflake is offering an analytic DBMS on a SaaS (Software as a Service) basis.
- The Snowflake DBMS is built from scratch (as opposed, to for example, being based on PostgreSQL or Hadoop).
- The Snowflake DBMS is columnar and append-only, as has become common for analytic RDBMS.
- Snowflake claims excellent SQL coverage for a 1.0 product.
- Snowflake, the company, has:
- 50 people.
- A similar number of current or past users.
- 5 referenceable customers.
- 2 techie founders out of Oracle, plus Marcin Zukowski.
- Bob Muglia as CEO.
Much of the Snowflake story can be summarized as cloud/elastic/simple/cheap.*
*Excuse me — inexpensive. Companies rarely like their products to be labeled as “cheap”.
In addition to its purely relational functionality, Snowflake accepts poly-structured data. Notes on that start:
- Ingest formats are JSON, XML or AVRO for now.
- I gather that the system automagically decides which fields/attributes are sufficiently repeated to be broken out as separate columns; also, there’s a column for the documents themselves.
I don’t know enough details to judge whether I’d call that an example of schema-on-need.
A key element of Snowflake’s poly-structured data story seems to be lateral views. I’m not too clear on that concept, but I gather: Read more
|Categories: Amazon and its cloud, Cloud computing, Data mart outsourcing, Data models and architecture, Data warehousing, Market share and customer counts, Parallelization, Pricing, Software as a Service (SaaS), Structured documents||3 Comments|
After visiting California recently, I made a flurry of posts, several of which generated considerable discussion.
- My claim that Spark will replace Hadoop MapReduce got much Twitter attention — including some high-profile endorsements — and also some responses here.
- My MemSQL post led to a vigorous comparison of MemSQL vs. VoltDB.
- My post on hardware and storage spawned a lively discussion of Hadoop hardware pricing; even Cloudera wound up disagreeing with what I reported Cloudera as having said. Sadly, there was less response to the part about the partial (!) end of Moore’s Law.
- My Cloudera/SQL/Impala/Hive apparently was well-balanced, in that it got attacked from multiple sides via Twitter & email. Apparently, I was too hard on Impala, I was too hard on Hive, and I was too hard on boxes full of cardboard file cards as well.
- My post on the Intel/Cloudera deal garnered a comment reminding us Dell had pushed the Intel distro.
- My CitusDB post picked up a few clarifying comments.
Here is a catch-all post to complete the set. Read more
I caught up with my clients at MongoDB to discuss the recent MongoDB 2.6, along with some new statements of direction. The biggest takeaway is that the MongoDB product, along with the associated MMS (MongoDB Management Service), is growing up. Aspects include:
- An actual automation and management user interface, as opposed to the current management style, which is almost entirely via scripts (except for the monitoring UI).
- That’s scheduled for public beta in May, and general availability later this year.
- It will include some kind of integrated provisioning with VMware, OpenStack, et al.
- One goal is to let you apply database changes, software upgrades, etc. without taking the cluster down.
- A reasonable backup strategy.
- A snapshot copy is made of the database.
- A copy of the log is streamed somewhere.
- Periodically — the default seems to be 6 hours — the log is applied to create a new current snapshot.
- For point-in-time recovery, you take the last snapshot prior to the point, and roll forward to the desired point.
- A reasonable locking strategy!
- Document-level locking is all-but-promised for MongoDB 2.8.
- That means what it sounds like. (I mention this because sometimes an XML database winds up being one big document, which leads to confusing conversations about what’s going on.)
- Security. My eyes glaze over at the details, but several major buzzwords have been checked off.
- A general code rewrite to allow for (more) rapid addition of future features.
Some technical background about Splunk
In an October, 2009 technical introduction to Splunk, I wrote (emphasis added):
Splunk software both reads logs and indexes them. The same code runs both on the nodes that do the indexing and on machines that simply emit logs.
It turns out that the bolded part was changed several years ago. However, I don’t have further details, so let’s move on to Splunk’s DBMS-like aspects.
I also wrote:
The fundamental thing that Splunk looks at is an increment to a log – i.e., whatever has been added to the log since Splunk last looked at it.
That remains true. Confusingly, Splunk refers to these log increments as “rows”, even though they’re really structured and queried more like documents.
I further wrote:
Splunk has a simple ILM (Information Lifecycle management) story based on time. I didn’t probe for details.
Splunk’s ILM story turns out to be simple indeed.
- As data streams in, Splunk adds it to the most recent — “hot” — bucket. Once a bucket is full, it becomes immutable — “warm” — and a new hot bucket is opened to receive data.
- Splunk executes queries against whichever of these time-slice buckets make sense, then unions results together as needed.
Finally, I wrote:
I get the impression that most Splunk entity extraction is done at search time, not at indexing time. Splunk says that, if a <name, value> pair is clearly marked, its software does a good job of recognizing same. Beyond that, fields seem to be specified by users when they define searches.
I have trouble understanding how Splunk could provide flexible and robust reporting unless it tokenized and indexed specific fields more aggressively than I think it now does.
The point of what I in October, 2013 called
a high(er)-performance data store into which you can selectively copy columns of data
and which Splunk enthusiastically calls its “High Performance Analytic Store” is to meet that latter need.
Inverted list technology is confusing for several reasons, which start: Read more
|Categories: Data models and architecture, NoSQL, SAP AG, Splunk, Structured documents, Text||1 Comment|
A couple of points that arise frequently in conversation, but that I don’t seem to have made clearly online.
“Metadata” is generally defined as “data about data”. That’s basically correct, but it’s easy to forget how many different kinds of metadata there are. My list of metadata kinds starts with:
- Data about data structure. This is the classical sense of the term. But please note:
- In a relational database, structural metadata is rather separate from the data itself.
- In a document database, each document might carry structure information with it.
- Other inputs to core data management functions. Two major examples are:
- Column statistics that inform RDBMS optimizers.
- Value ranges that inform partition pruning or, more generally, data skipping.
- Inputs to ancillary data management functions — for example, security privileges.
- Support for human decisions about data — for example, information about authorship or lineage.
What’s worse, the past year’s most famous example of “metadata”, telephone call metadata, is misnamed. This so-called metadata, much loved by the NSA (National Security Agency), is just data, e.g. in the format of a CDR (Call Detail Record). Calling it metadata implies that it describes other data — the actual contents of the phone calls — that the NSA strenuously asserts don’t actually exist.
And finally, the first bullet point above has a counter-intuitive consequence — all common terminology notwithstanding, relational data is less structured than document data. Reasons include:
- Relational databases usually just hold strings — or maybe numbers — with structural information being held elsewhere.
- Some document databases store structural metadata right with the document data itself.
- Some document databases store data in the form of (name, value) pairs. In some cases additional structure is imposed by naming conventions.
- Actual text documents carry the structure imposed by grammar and syntax.
- A lengthy survey of metadata kinds, biased to Hadoop (August, 2012)
- Metadata as derived data (May, 2011)
- Dataset management (May, 2013)
- Structured/unstructured … multi-structured/poly-structured (May, 2011)
|Categories: Data models and architecture, Hadoop, Structured documents, Surveillance and privacy, Telecommunications||5 Comments|
The 2013 Gartner Magic Quadrant for Operational Database Management Systems is out. “Operational” seems to be Gartner’s term for what I call short-request, in each case the point being that OLTP (OnLine Transaction Processing) is a dubious term when systems omit strict consistency, and when even strictly consistent systems may lack full transactional semantics. As is usually the case with Gartner Magic Quadrants:
- I admire the raw research.
- The opinions contained are generally reasonable (especially since Merv Adrian joined the Gartner team).
- Some of the details are questionable.
- There’s generally an excessive focus on Gartner’s perception of vendors’ business skills, and on vendors’ willingness to parrot all the buzzphrases Gartner wants to hear.
- The trends Gartner highlights are similar to those I see, although our emphasis may be different, and they may leave some important ones out. (Big omission — support for lightweight analytics integrated into operational applications, one of the more genuine forms of real-time analytics.)
Anyhow: Read more
I coined the term schema-on-need last month. More precisely, I coined it while being briefed on JSON-in-Teradata, which was announced earlier this week, and is slated for availability in the first half of 2014.
The basic JSON-in-Teradata story is as you expect:
- A JSON document is stuck into a relational field.
(Oddly, Teradata wasn’t yet sure whether the field would be a BLOB or VARCHAR or something else.)Edit: See Dan Graham’s comment below.
- Fields within the JSON document can be indexed on.
- Those fields can be referenced in SQL statements much as regular Teradata columns can.
You have to retrieve the whole document.Edit: See Dan Graham’s comment below.
- To avert the performance pain of retrieving the whole document, you can of course copy any particular field into a column of its own. (That’s the schema-on-need part of the story.)
JSON virtual columns are referenced a little differently than ordinary physical columns are. Thus, if you materialize a virtual column, you have to change your SQL. If you’re doing business intelligence through a semantic layer, or otherwise have some kind of declarative translation, that’s probably not a big drawback. If you’re coding analytic procedures directly, it still may not be a big drawback — hopefully you won’t reference the virtual column too many times in code before you decide to materialize it instead.
My Bobby McFerrin* imitation notwithstanding, Hadapt illustrates a schema-on-need approach that is slicker than Teradata’s in two ways. First, Hadapt has full SQL transparency between virtual and physical columns. Second, Hadapt handles not just JSON, but anything represented by key-value pairs. Still, like XML before it but more concisely, JSON is a pretty versatile data interchange format. So JSON-in-Teradata would seem to be useful as it stands.
*The singer in the classic 1988 music video Don’t Worry Be Happy. The other two performers, of course, were Elton John and Robin Williams.
|Categories: Data models and architecture, Data warehousing, Hadapt, Schema on need, Structured documents, Teradata||3 Comments|
There’s a growing trend for DBMS to beef up their support for multiple data manipulation languages (DMLs) or APIs — and there’s a special boom in JSON support, MongoDB-compatible or otherwise. So I talked earlier tonight with IBM’s Bobbie Cochrane about how JSON is managed in DB2.
For starters, let’s note that there are at least four strategies IBM could have used.
- Store JSON in a BLOB (Binary Large OBject) or similar existing datatype. That’s what IBM actually chose.
- Store JSON in a custom datatype, using the datatype extensibility features DB2 has had since the 1990s. IBM is not doing this, and doesn’t see a need to at this time.
- Use DB2 pureXML, along with some kind of JSON/XML translator. DB2 managed JSON this way in the past, via UDFs (User-Defined Functions), but that implementation is superseded by the new BLOB-based approach, which offers better performance in ingest and query alike.
- Shred — to use a term from XML days — JSON into a bunch of relational columns. IBM experimented with this approach, but ultimately rejected it. In dismissing shredding, Bobbie also disdained any immediate support for schema-on-need.
IBM’s technology choices are of course influenced by its use case focus. It’s reasonable to divide MongoDB use cases into two large buckets:
- Hardcore internet and/or machine-generated data, for example from a website.
- Enterprise data aggregation, for example a “360-degree customer view.”
IBM’s DB2 JSON features are targeted at the latter bucket. Also, I suspect that IBM is generally looking for a way to please users who enjoy working on and with their MongoDB skills. Read more
|Categories: Data models and architecture, IBM and DB2, MongoDB, NoSQL, pureXML, Structured documents||2 Comments|
Two years ago I wrote about how Zynga managed analytic data:
Data is divided into two parts. One part has a pretty ordinary schema; the other is just stored as a huge list of name-value pairs. (This is much like eBay‘s approach with its Teradata-based Singularity, except that eBay puts the name-value pairs into long character strings.) … Zynga adds data into the real schema when it’s clear it will be needed for a while.
What was then the province of a few huge web companies is now poised to be a broader trend. Specifically:
- Relational DBMS are adding or enhancing their support for complex datatypes, to accommodate various kinds of machine-generated data.
- MongoDB-compatible JSON is the flavor of the day on the short-request side, but alternatives include other JSON, XML, other key-value, or text strings.
- It is often possible to index on individual attributes inside the complex datatype.
- The individual attributes inside the complex datatypes amount to virtual columns, which can play similar roles in SQL statements as physical columns do.
- Over time, the DBA may choose to materialize virtual columns as additional physical columns, to boost query performance.
That migration from virtual to physical columns is what I’m calling “schema-on-need”. Thus, schema-on-need is what you invoke when schema-on-read no longer gets the job done.
|Categories: Data models and architecture, Data warehousing, MongoDB, PostgreSQL, Schema on need, Structured documents||10 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