Discussion of MongoDB and its sponsoring company 10gen.
- Question: Why do policemen work in pairs?
- Answer: One to read and one to write.
A lot has happened in MongoDB technology over the past year. For starters:
- The big news in MongoDB 3.0* is the WiredTiger storage engine. The top-level claims for that are that one should “typically” expect (individual cases can of course vary greatly):
- 7-10X improvement in write performance.
- No change in read performance (which however was boosted in MongoDB 2.6).
- ~70% reduction in data size due to compression (disk only).
- ~50% reduction in index size due to compression (disk and memory both).
- MongoDB has been adding administration modules.
- A remote/cloud version came out with, if I understand correctly, MongoDB 2.6.
- An on-premise version came out with 3.0.
- They have similar features, but are expected to grow apart from each other over time. They have different names.
*Newly-released MongoDB 3.0 is what was previously going to be MongoDB 2.8. My clients at MongoDB finally decided to give a “bigger” release a new first-digit version number.
To forestall confusion, let me quickly add: Read more
|Categories: Database compression, Hadoop, Humor, In-memory DBMS, MongoDB, NoSQL, Open source, Structured documents, Sybase||9 Comments|
I hoped to write a reasonable overview of current- to medium-term future IT innovation. Yeah, right. But if we abandon any hope that this post could be comprehensive, I can at least say:
1. Back in 2011, I ranted against the term Big Data, but expressed more fondness for the V words — Volume, Velocity, Variety and Variability. That said, when it comes to data management and movement, solutions to the V problems have generally been sketched out.
- Volume has been solved. There are Hadoop installations with 100s of petabytes of data, analytic RDBMS with 10s of petabytes, general-purpose Exadata sites with petabytes, and 10s/100s of petabytes of analytic Accumulo at the NSA. Further examples abound.
- Velocity is being solved. My recent post on Hadoop-based streaming suggests how. In other use cases, velocity is addressed via memory-centric RDBMS.
- Variety and Variability have been solved. MongoDB, Cassandra and perhaps others are strong NoSQL choices. Schema-on-need is in earlier days, but may help too.
2. Even so, there’s much room for innovation around data movement and management. I’d start with:
- Product maturity is a huge issue for all the above, and will remain one for years.
- Hadoop and Spark show that application execution engines:
- Have a lot of innovation ahead of them.
- Are tightly entwined with data management, and with data movement as well.
- Hadoop is due for another refactoring, focused on both in-memory and persistent storage.
- There are many issues in storage that can affect data technologies as well, including but not limited to:
- Solid-state (flash or post-flash) vs. spinning disk.
- Networked vs. direct-attached.
- Virtualized vs. identifiable-physical.
- Graph analytics and data management are still confused.
I’m taking a few weeks defocused from work, as a kind of grandpaternity leave. That said, the venue for my Dances of Infant Calming is a small-but-nice apartment in San Francisco, so a certain amount of thinking about tech industries is inevitable. I even found time last Tuesday to meet or speak with my clients at WibiData, MemSQL, Cloudera, Citus Data, and MongoDB. And thus:
1. I’ve been sloppy in my terminology around “geo-distribution”, in that I don’t always make it easy to distinguish between:
- Storing different parts of a database in different geographies, often for reasons of data privacy regulatory compliance.
- Replicating an entire database into different geographies, often for reasons of latency and/or availability/ disaster recovery,
The latter case can be subdivided further depending on whether multiple copies of the data can accept first writes (aka active-active, multi-master, or multi-active), or whether there’s a clear single master for each part of the database.
What made me think of this was a phone call with MongoDB in which I learned that the limit on number of replicas had been raised from 12 to 50, to support the full-replication/latency-reduction use case.
2. Three years ago I posted about agile (predictive) analytics. One of the points was:
… if you change your offers, prices, ad placement, ad text, ad appearance, call center scripts, or anything else, you immediately gain new information that isn’t well-reflected in your previous models.
Subsequently I’ve been hearing more about predictive experimentation such as bandit testing. WibiData, whose views are influenced by a couple of Very Famous Department Store clients (one of which is Macy’s), thinks experimentation is quite important. And it could be argued that experimentation is one of the simplest and most direct ways to increase the value of your data.
3. I’d further say that a number of developments, trends or possibilities I’m seeing are or could be connected. These include agile and experimental predictive analytics in general, as noted in the previous point, along with: Read more
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.
Cassandra’s reputation in many quarters is:
- World-leading in the geo-distribution feature.
- Impressively scalable.
- Hard to use.
This has led competitors to use, and get away with, sales claims along the lines of “Well, if you really need geo-distribution and can’t wait for us to catch up — which we soon will! — you should use Cassandra. But otherwise, there are better choices.”
My friends at DataStax, naturally, don’t think that’s quite fair. And so I invited them — specifically Billy Bosworth and Patrick McFadin — to educate me. Here are some highlights of that exercise.
DataStax and Cassandra have some very impressive accounts, which don’t necessarily revolve around geo-distribution. Netflix, probably the flagship Cassandra user — since Cassandra inventor Facebook adopted HBase instead — actually hasn’t been using the geo-distribution feature. Confidential accounts include:
- A petabyte or so of data at a very prominent company, geo-distributed, with 800+ nodes, in a kind of block storage use case.
- A messaging application at a very prominent company, anticipated to grow to multiple data centers and a petabyte of so of data, across 1000s of nodes.
- A 300 terabyte single-data-center telecom account (which I can’t find on DataStax’s extensive customer list).
- A huge health records deal.
- A Fortune 10 company.
DataStax and Cassandra won’t necessarily win customer-brag wars versus MongoDB, Couchbase, or even HBase, but at least they’re strongly in the competition.
DataStax claims that simplicity is now a strength. There are two main parts to that surprising assertion. Read more
|Categories: Cassandra, Clustering, Couchbase, Data models and architecture, DataStax, Facebook, HBase, Health care, Log analysis, Market share and customer counts, MongoDB, NoSQL, Petabyte-scale data management, Specific users||10 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|
The general Tokutek strategy has always been:
- Write indexes efficiently, which …
- … makes it reasonable to have more indexes, which …
- … lets more queries run fast.
But the details of “writes indexes efficiently” have been hard to nail down. For example, my post about Tokutek indexing last January, while not really mistaken, is drastically incomplete.
Adding further confusion is that Tokutek now has two product lines:
- TokuDB, a MySQL storage engine.
- TokuMX, in which the parts of MongoDB 2.2 that roughly equate to a storage engine are ripped out and replaced with Tokutek code.
TokuMX further adds language support for transactions and a rewrite of MongoDB’s replication code.
So let’s try again. I had a couple of conversations with Martin Farach-Colton, who:
- Is a Tokutek co-founder.
- Stayed in academia.
- Is a data structures guy, not a database expert per se.
The core ideas of Tokutek’s architecture start: Read more
I lampoon the word “disruptive” for being badly overused. On the other hand, I often refer to the concept myself. Perhaps I should clarify.
- Market leaders serve high-end customers with complex, high-end products and services, often distributed through a costly sales channel.
- Upstarts serve a different market segment, often cheaply and/or simply, perhaps with a different business model (e.g. a different sales channel).
- Upstarts expand their offerings, and eventually attack the leaders in their core markets.
In response (this is the Innovator’s Solution part):
- Leaders expand their product lines, increasing the value of their offerings in their core markets.
- In particular, leaders expand into adjacent market segments, capturing margins and value even if their historical core businesses are commoditized.
- Leaders may also diversify into direct competition with the upstarts, but that generally works only if it’s via a separate division, perhaps acquired, that has permission to compete hard with the main business.
But not all cleverness is “disruption”.
- Routine product advancement by leaders — even when it’s admirably clever — is “sustaining” innovation, as opposed to the disruptive stuff.
- Innovative new technology from small companies is not, in itself, disruption either.
Here are some of the examples that make me think of the whole subject. Read more
|Categories: Business intelligence, Data warehousing, Hadoop, Microsoft and SQL*Server, MongoDB, MySQL, Netezza, NewSQL, NoSQL, Oracle, Predictive modeling and advanced analytics, QlikTech and QlikView, Tableau Software||13 Comments|