Schema on need
Discussion and analysis of support for what we call the schema-on-need feature.
It took me a bit of time, and an extra call with Vertica’s long-time R&D chief Shilpa Lawande, but I think I have a decent handle now on Vertica 7, code-named Crane. The two aspects of Vertica 7 I find most interesting are:
- Flex Zone, a schema-on-need technology very much like Hadapt’s (but of course with access to Vertica performance).
- What sounds like an alternate query execution capability for short-request queries, the big point of which is that it saves them from being broadcast across the whole cluster, hence improving scalability. (Adding nodes of course doesn’t buy you much for the portion of a workload that’s broadcast.)
Other Vertica 7 enhancements include:
- A lot of Bottleneck Whack-A-Mole.
- “Significant” improvements to the Vertica management console.
- Security enhancements (Kerberos), Hadoop integration enhancements (HCatalog), and enhanced integration with Hadoop security (Kerberos again).
- Some availability hardening. (“Fault groups”, which for example let you ensure that data is replicated not just to 2+ nodes, but also that the nodes aren’t all on the same rack.)
- Java as an option to do in-database analytics. (Who knew that feature was still missing?)
- Some analytic functionality. (Approximate COUNT DISTINCT, but not yet Approximate MEDIAN.)
Overall, two recurring themes in our discussion were:
- Load and ETL (Extract/Transform/Load) performance, and/or obviating ETL.
- Short-request performance, in the form of more scalable short-request concurrency.
I’m a little shaky on embargo details — but I do know what was in my own quote in a Splunk press release that went out yesterday.
Splunk has been rolling out a lot of news. In particular:
- Hunk follows through on the Hadoop/Splunk (get it?) co-opetition I foreshadowed last year, including access to Hadoop via the same tools that run over the Splunk data store, plus …
- … some Datameer-like capabilities to view partial Hadoop-job results as they flow in.
- Splunk 6 has lots of new features, including a bunch of better please-don’t-call-it-BI capabilities, and …
- … a high(er)-performance data store into which you can selectively copy columns of data.
I imagine there are some operationally-oriented use cases for which Splunk instantly offers the best Hadoop business intelligence choice available. But what I really think is cool is Splunk’s schema-on-need story, wherein:
- Data comes in wholly schema-less, in a time series of text snippets.
- Some of the fields in the text snippets are indexed for faster analysis, automagically or upon user decree.
- All this can now happen over the Splunk data store or (new option) over Hadoop.
- Fields can (in another new option) also be copied to a separate data store, claimed to be of much higher performance.
That highlights a pretty serious and flexible vertical analytic stack. I like it.
|Categories: Business intelligence, Data models and architecture, Data warehousing, Hadoop, Schema on need, Splunk||1 Comment|
Glassbeam checked in recently, and they turn out to exemplify quite a few of the themes I’ve been writing about. For starters:
- Glassbeam has an analytic technology stack focused on poly-structured machine-generated data.
- Glassbeam partially organizes that data into event series …
- … in a schema that is modified as needed.
Glassbeam basics include:
- Founded in 2009.
- Based in Santa Clara. Back-end engineering in Bangalore.
- $6 million in angel money; no other VC.
- High single-digit customer count, …
- … plus another high single-digit number of end customers for an OEM offering a limited version of their product.
All Glassbeam customers except one are SaaS/cloud (Software as a Service), and even that one was only offered a subscription (as oppose to perpetual license) price.
So what does Glassbeam’s technology do? Glassbeam says it is focused on “machine data analytics,” specifically for the “Internet of Things”, which it distinguishes from IT logs.* Specifically, Glassbeam sells to manufacturers of complex devices — IT (most of its sales so far ), medical, automotive (aspirational to date), etc. — and helps them analyze “phone home” data, for both support/customer service and marketing kinds of use cases. As of a recent release, the Glassbeam stack can: 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||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.