Analysis of data management technology optimized for specific datatypes, such as text, geospatial, object, RDF, or XML. Related subjects include:
- Any subcategory
- Database diversity
Obviously, a large fraction of what I write about involves technical differentiation. So let’s try for a framework where differentiation claims can be placed in context. This post will get through the generalities. The sequels will apply them to specific cases.
Many buying and design considerations for IT fall into six interrelated areas: Read more
I last wrote about Couchbase in November, 2012, around the time of Couchbase 2.0. One of the many new features I mentioned then was secondary indexing. Ravi Mayuram just checked in to tell me about Couchbase 4.0. One of the important new features he mentioned was what I think he said was Couchbase’s “first version” of secondary indexing. Obviously, I’m confused.
Now that you’re duly warned, let me remind you of aspects of Couchbase timeline.
- 2 corporate name changes ago, Couchbase was organized to commercialize memcached. memcached, of course, was internet companies’ default way to scale out short-request processing before the rise of NoSQL, typically backed by manually sharded MySQL.
- Couchbase’s original value proposition, under the name Membase, was to provide persistence and of course support for memcached. This later grew into a caching-oriented pitch even to customers who weren’t already memcached users.
- A merger with the makers of CouchDB ensued, with the intention of replacing Membase’s SQLite back end with CouchDB at the same time as JSON support was introduced. This went badly.
- By now, however, Couchbase sells for more than distributed cache use cases. Ravi rattled off a variety of big-name customer examples for system-of-record kinds of use cases, especially in session logging (duh) and also in travel reservations.
- Couchbase 4.0 has been in beta for a few months.
Technical notes on Couchbase 4.0 — and related riffs — start: Read more
|Categories: Cache, Clustering, Couchbase, Data models and architecture, Databricks, Spark and BDAS, Exadata, Hadoop, MarkLogic, MongoDB, MySQL, NoSQL, Open source, Schema on need, Structured documents, Web analytics||1 Comment|
MongoDB isn’t the only company I reached out to recently for an update. Another is DataStax. I chatted mainly with Patrick McFadin, somebody with whom I’ve had strong consulting relationships at a user and vendor both. But Rachel Pedreschi contributed the marvelous phrase “twinkling dashboard”.
It seems fair to say that in most cases:
- Cassandra is adopted for operational applications, specifically ones with requirements for extreme uptime and/or extreme write speed. (Of course, it should also be the case that NoSQL data structures are a good fit.)
- Spark, including SparkSQL, and Solr are seen primarily as ways to navigate or analyze the resulting data.
Those generalities, in my opinion, make good technical sense. Even so, there are some edge cases or counterexamples, such as:
- DataStax trumpets British Gas‘ plans collecting a lot of sensor data and immediately offering it up for analysis.*
- Safeway uses Cassandra for a mobile part of its loyalty program, scoring customers and pushing coupons at them.
- A large title insurance company uses Cassandra-plus-Solr to manage a whole lot of documents.
*And so a gas company is doing lightweight analysis on boiler temperatures, which it regards as hot data.
While most of the specifics are different, I’d say similar things about MongoDB, Cassandra, or any other NoSQL DBMS that comes to mind: Read more
|Categories: Business intelligence, Cassandra, Databricks, Spark and BDAS, DataStax, NoSQL, Open source, Petabyte-scale data management, Predictive modeling and advanced analytics, Specific users, Text||6 Comments|
One pleasure in talking with my clients at MongoDB is that few things are NDA. So let’s start with some numbers:
- >2,000 named customers, the vast majority of which are unique organizations who do business with MongoDB directly.
- ~75,000 users of MongoDB Cloud Manager.
- Estimated ~1/4 million production users of MongoDB total.
Also >530 staff, and I think that number is a little out of date.
MongoDB lacks many capabilities RDBMS users take for granted. MongoDB 3.2, which I gather is slated for early November, narrows that gap, but only by a little. Features include:
- Some JOIN capabilities.
- Specifically, these are left outer joins, so they’re for lookup but not for filtering.
- JOINs are not restricted to specific shards of data …
- … but do benefit from data co-location when it occurs.
- A BI connector. Think of this as a MongoDB-to- SQL translator. Using this does require somebody to go in and map JSON schemas and relational tables to each other. Once that’s done, the flow is:
- Basic SQL comes in.
- Filters and GroupBys are pushed down to MongoDB. A result set … well, it results.
- The result set is formatted into a table and returned to the system — for example a business intelligence tool — that sent the SQL.
- Database-side document validation, in the form of field-specific rules that combine into a single expression against which to check a document.
- This is fairly simple stuff — no dependencies among fields in the same document, let alone foreign key relationships.
- MongoDB argues, persuasively, that this simplicity makes it unlikely to recreate the spaghetti code maintenance nightmare that was 1990s stored procedures.
- MongoDB concedes that, for performance, it will ordinarily be a good idea to still do your validation on the client side.
- MongoDB points out that enforcement can be either strict (throw errors) or relaxed (just note invalid documents to a log). The latter option is what makes it possible to install this feature without breaking your running system.
There’s also a closed-source database introspection tool coming, currently codenamed MongoDB Scout. Read more
|Categories: Business intelligence, EAI, EII, ETL, ELT, ETLT, Market share and customer counts, MongoDB, NoSQL, Open source, Structured documents, Text||6 Comments|
- Multi-model database management has been around for decades. Marketers who say otherwise are being ridiculous.
- Thus, “multi-model”-centric marketing is the last refuge of the incompetent. Vendors who say “We have a great DBMS, and by the way it’s multi-model (now/too)” are being smart. Vendors who say “You need a multi-model DBMS, and that’s the reason you should buy from us” are being pathetic.
- Multi-logical-model data management and multi-latency-assumption data management are greatly intertwined.
Before supporting my claims directly, let me note that this is one of those posts that grew out of a Twitter conversation. The first round went:
Merv Adrian: 2 kinds of multimodel from DBMS vendors: multi-model DBMSs and multimodel portfolios. The latter create more complexity, not less.
Me: “Owned by the same vendor” does not imply “well integrated”. Indeed, not a single example is coming to mind.
Merv: We are clearly in violent agreement on that one.
Around the same time I suggested that Intersystems Cache’ was the last significant object-oriented DBMS, only to get the pushback that they were “multi-model” as well. That led to some reasonable-sounding justification — although the buzzwords of course aren’t from me — namely: Read more
|Categories: Complex event processing (CEP), Data models and architecture, Database diversity, Databricks, Spark and BDAS, Intersystems and Cache', MOLAP, Object||3 Comments|
It’s difficult to project the rate of IT change in health care, because:
- Health care is suffused with technology — IT, medical device and biotech alike — and hence has the potential for rapid change. However, it is also the case that …
- … health care is heavily bureaucratic, political and regulated.
Timing aside, it is clear that health care change will be drastic. The IT part of that starts with vastly comprehensive electronic health records, which will be accessible (in part or whole as the case may be) by patients, care givers, care payers and researchers alike. I expect elements of such records to include:
- The human-generated part of what’s in ordinary paper health records today, but across a patient’s entire lifetime. This of course includes notes created by doctors and other care-givers.
- Large amounts of machine-generated data, including:
- The results of clinical tests. Continued innovation can be expected in testing, for reasons that include:
- Most tests exploit electronic technology. Progress in electronics is intense.
- Biomedical research is itself intense.
- In particular, most research technologies (for example gene sequencing) can be made cheap enough over time to be affordable clinically.
- The output of consumer health-monitoring devices — e.g. Fitbit and its successors. The buzzword here is “quantified self”, but what it boils down to is that every moment of our lives will be measured and recorded.
- The results of clinical tests. Continued innovation can be expected in testing, for reasons that include:
These vastly greater amounts of data cited above will allow for greatly changed analytics.
I talked with my clients at MemSQL about the release of MemSQL 4.0. Let’s start with the reminders:
- MemSQL started out as in-memory OTLP (OnLine Transaction Processing) DBMS …
- … but quickly positioned with “We also do ‘real-time’ analytic processing” …
- … and backed that up by adding a flash-based column store option …
- … before Gartner ever got around to popularizing the term HTAP (Hybrid Transaction and Analytic Processing).
- There’s also a JSON option.
The main new aspects of MemSQL 4.0 are:
- Geospatial indexing. This is for me the most interesting part.
- A new optimizer and, I suppose, query planner …
- … which in particular allow for serious distributed joins.
- Some rather parallel-sounding connectors to Spark. Hadoop and Amazon S3.
- Usual-suspect stuff including:
- More SQL coverage (I forgot to ask for details).
- Some added or enhanced administrative/tuning/whatever tools (again, I forgot to ask for details).
- Surely some general Bottleneck Whack-A-Mole.
There’s also a new free MemSQL “Community Edition”. MemSQL hopes you’ll experiment with this but not use it in production. And MemSQL pricing is now wholly based on RAM usage, so the column store is quasi-free from a licensing standpoint is as well.
|Categories: Amazon and its cloud, Columnar database management, Databricks, Spark and BDAS, GIS and geospatial, Hadoop, Investment research and trading, Market share and customer counts, MemSQL, NewSQL, Pricing, Structured documents||8 Comments|
I’m going to be out-of-sorts this week, due to a colonoscopy. (Between the prep, the procedure, and the recovery, that’s a multi-day disablement.) In the interim, here’s a collection of links, quick comments and the like.
1. Are you an engineer considering a start-up? This post is for you. It’s based on my long experience in and around such scenarios, and includes a section on “Deadly yet common mistakes”.
2. There seems to be a lot of confusion regarding the business model at my clients Databricks. Indeed, my own understanding of Databricks’ on-premises business has changed recently. There are no changes in my beliefs that:
- Databricks does not directly license or support on-premises Spark users. Rather …
- … it helps partner companies to do so, where:
- Examples of partner companies include usual-suspect Hadoop distribution vendors, and DataStax.
- “Help” commonly includes higher-level support.
However, I now get the impression that revenue from such relationships is a bigger deal to Databricks than I previously thought.
Databricks, by the way, has grown to >50 people.
3. DJ Patil and Ruslan Belkin apparently had a great session on lessons learned, covering a lot of ground. Many of the points are worth reading, but one in particular echoed something I’m hearing lots of places — “Data is super messy, and data cleanup will always be literally 80% of the work.” Actually, I’d replace the “always” by something like “very often”, and even that mainly for newish warehouses, data marts or datasets. But directionally the comment makes a whole lot of sense.
|Categories: Data integration and middleware, Databricks, Spark and BDAS, DataStax, Hadoop, Health care, Investment research and trading, Text||Leave a Comment|
Indexes are central to database management.
- My first-ever stock analyst report, in 1982, correctly predicted that index-based DBMS would supplant linked-list ones …
- … and to this day, if one wants to retrieve a small fraction of a database, indexes are generally the most efficient way to go.
- Recently, I’ve had numerous conversations in which indexing strategies played a central role.
Perhaps it’s time for a round-up post on indexing.
1. First, let’s review some basics. Classically:
- An index is a DBMS data structure that you probe to discover where to find the data you really want.
- Indexes make data retrieval much more selective and hence faster.
- While indexes make queries cheaper, they make writes more expensive — because when you write data, you need to update your index as well.
- Indexes also induce costs in database size and administrative efforts. (Manual index management is often the biggest hurdle for “zero-DBA” RDBMS installations.)
2. Further: Read more
|Categories: Data warehousing, Database compression, GIS and geospatial, Google, MapReduce, McObject, MemSQL, MySQL, ScaleDB, solidDB, Sybase, Text, Tokutek and TokuDB||18 Comments|
Over the past couple years, there have been various quick comments and vague press releases about “BI for NoSQL”. I’ve had trouble, however, imagining what it could amount to that was particularly interesting, with my confusion boiling down to “Just what are you aggregating over what?” Recently I raised the subject with a few leading NoSQL companies. The result is that my confusion was expanded. Here’s the small amount that I have actually figured out.
As I noted in a recent post about data models, many databases — in particular SQL and NoSQL ones — can be viewed as collections of <name, value> pairs.
- In a relational database, a record is a collection of <name, value> pairs with a particular and predictable — i.e. derived from the table definition — sequence of names. Further, a record usually has an identifying key (commonly one of the first values).
- Something similar can be said about structured-document stores — i.e. JSON or XML — except that the sequence of names may not be consistent from one document to the next. Further, there’s commonly a hierarchical relationship among the names.
- For these purposes, a “wide-column” NoSQL store like Cassandra or HBase can be viewed much as a structured-document store, albeit with different performance optimizations and characteristics and a different flavor of DML (Data Manipulation Language).
Consequently, a NoSQL database can often be viewed as a table or a collection of tables, except that:
- The NoSQL database is likely to have more null values.
- The NoSQL database, in a naive translation toward relational, may have repeated values. So a less naive translation might require extra tables.
That’s all straightforward to deal with if you’re willing to write scripts to extract the NoSQL data and transform or aggregate it as needed. But things get tricky when you try to insist on some kind of point-and-click. And by the way, that last comment pertains to BI and ETL (Extract/Transform/Load) alike. Indeed, multiple people I talked with on this subject conflated BI and ETL, and they were probably right to do so.
|Categories: Business intelligence, Cassandra, EAI, EII, ETL, ELT, ETLT, HBase, MongoDB, NoSQL, Structured documents||5 Comments|