Theory and architecture
Analysis of design choices in databases and database management systems. Related subjects include:
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|
1. The rise of SAP (and later Siebel Systems) was greatly helped by Anderson Consulting, even before it was split off from the accounting firm and renamed as Accenture. My main contact in that group was Rob Kelley, but it’s possible that Brian Sommer was even more central to the industry-watching part of the operation. Brian is still around, and he just leveled a blast at the ERP* industry, which I encourage you to read. I agree with most of it.
*Enterprise Resource Planning
Brian’s argument, as I interpret it, boils down mainly to two points:
- Big ERP companies selling big ERP systems are pathetically slow at adding new functionality. He’s right. My favorite example is the multi-decade slog to integrate useful analytics into operational apps.
- The world of “Big Data” is fundamentally antithetical to the design of current-generation ERP systems. I think he’s right in that as well.
I’d add that SaaS (Software As A Service)/on-premises tensions aren’t helping incumbent vendors either.
But no article addresses all the subjects it ideally should, and I’d like to call out two omissions. First, what Brian said is in many cases applicable just to large and/or internet-first companies. Plenty of smaller, more traditional businesses could get by just fine with no more functionality than is in “Big ERP” today, if we stipulate that it should be:
- Delivered via SaaS.
- Much easier to adopt and use.
This is part of a three-post series on Kudu, a new data storage system from Cloudera.
- Part 1 is an overview of Kudu technology.
- Part 2 (this post) is a lengthy dive into how Kudu writes and reads data.
- Part 3 is a brief speculation as to Kudu’s eventual market significance.
Let’s talk in more detail about how Kudu stores data.
- As previously noted, inserts land in an in-memory row store, which is periodically flushed to the column store on disk. Queries are federated between these two stores. Vertica taught us to call these the WOS (Write-Optimized Store) and ROS (Read-Optimized Store) respectively, and I’ll use that terminology here.
- Part of the ROS is actually another in-memory store, aka the DeltaMemStore, where updates and deletes land before being applied to the DiskRowSets. These stores are managed separately for each DiskRowSet. DeltaMemStores are checked at query time to confirm whether what’s in the persistent store is actually up to date.
- A major design goal for Kudu is that compaction should never block – nor greatly slow — other work. In support of that:
- Compaction is done, server-by-server, via a low-priority but otherwise always-on background process.
- There is a configurable maximum to how big a compaction process can be — more precisely, the limit is to how much data the process can work on at once. The current default figure = 128 MB, which is 4X the size of a DiskRowSet.
- When done, Kudu runs a little optimization to figure out which 128 MB to compact next.
- Every tablet has its own write-ahead log.
- This creates a practical limitation on the number of tablets …
- … because each tablet is causing its own stream of writes to “disk” …
- … but it’s only a limitation if your “disk” really is all spinning disk …
- … because multiple simultaneous streams work great with solid-state memory.
- Log retention is configurable, typically the greater of 5 minutes or 128 MB.
- Metadata is cached in RAM. Therefore:
- ALTER TABLE kinds of operations that can be done by metadata changes only — i.e. adding/dropping/renaming columns — can be instantaneous.
- To keep from being screwed up by this, the WOS maintains a column that labels rows by which schema version they were created under. I immediately called this MSCC — Multi-Schema Concurrency Control — and Todd Lipcon agreed.
- Durability, as usual, boils down to “Wait until a quorum has done the writes”, with a configurable option as to what constitutes a “write”.
- Servers write to their respective write-ahead logs, then acknowledge having done so.
- If it isn’t too much of a potential bottleneck — e.g. if persistence is on flash — the acknowledgements may wait until the log has been fsynced to persistent storage.
- There’s a “thick” client library which, among other things, knows enough about the partitioning scheme to go straight to the correct node(s) on a cluster.
|Categories: Cloudera, Columnar database management, Hadoop, Solid-state memory, SQL/Hadoop integration||25 Comments|
This is part of a three-post series on Kudu, a new data storage system from Cloudera.
- Part 1 (this post) is an overview of Kudu technology.
- Part 2 is a lengthy dive into how Kudu writes and reads data.
- Part 3 is a brief speculation as to Kudu’s eventual market significance.
Cloudera is introducing a new open source project, Kudu,* which from Cloudera’s standpoint is meant to eventually become the single best underpinning for analytics on the Hadoop stack. I’ve spent multiple hours discussing Kudu with Cloudera, mainly with Todd Lipcon. Any errors are of course entirely mine.
*Like the impala, the kudu is a kind of antelope. I knew that, because I enjoy word games. What I didn’t know — and which is germane to the naming choice — is that the kudu has stripes.
- Kudu is an alternative to HDFS (Hadoop Distributed File System), or to HBase.
- Kudu is meant to be the underpinning for Impala, Spark and other analytic frameworks or engines.
- Kudu is not meant for OLTP (OnLine Transaction Processing), at least in any foreseeable release. For example:
- Kudu doesn’t support multi-row transactions.
- There are no active efforts to front-end Kudu with an engine that is fast at single-row queries.
- Kudu is rather columnar, except for transitory in-memory stores.
- Kudu’s core design points are that it should:
- Accept data very quickly.
- Immediately make that data available for analytics.
- More specifically, Kudu is meant to accept, along with slower forms of input:
- Lots of fast random writes, e.g. of web interactions.
- Streams, viewed as a succession of inserts.
- Updates and inserts alike.
- The core “real-time” use cases for which Kudu is designed are, unsurprisingly:
- Low-latency business intelligence.
- Predictive model scoring.
- Kudu is designed to work fine with spinning disk, and indeed has been tested to date mainly on disk-only nodes. Even so, Kudu’s architecture is optimized for the assumption that there will be at least some flash on the node.
- Kudu is designed primarily to support relational/SQL processing. However, Kudu also has a nested-data roadmap, which of course starts with supporting the analogous capabilities in Impala.
|Categories: Business intelligence, Cloudera, Columnar database management, Database compression, Databricks, Spark and BDAS, Hadoop, HBase, Predictive modeling and advanced analytics, Solid-state memory, SQL/Hadoop integration||7 Comments|
- My client Rocana is the renamed ScalingData, where Rocana is meant to signify ROot Cause ANAlysis.
- Rocana was founded by Omer Trajman, who I’ve referenced numerous times in the past, and who I gather is a former boss of …
- … cofounder Eric Sammer.
- Rocana recently told me it had 35 people.
- Rocana has a very small number of quite large customers.
Rocana portrays itself as offering next-generation IT operations monitoring software. As you might expect, this has two main use cases:
- Actual operations — figuring out exactly what isn’t working, ASAP.
Rocana’s differentiation claims boil down to fast and accurate anomaly detection on large amounts of log data, including but not limited to:
- The sort of network data you’d generally think of — “everything” except packet-inspection stuff.
- Firewall output.
- Database server logs.
- Point-of-sale data (at a retailer).
- “Application data”, whatever that means. (Edit: See Tom Yates’ clarifying comment below.)
|Categories: Business intelligence, Hadoop, Kafka and Confluent, Log analysis, Market share and customer counts, Petabyte-scale data management, Predictive modeling and advanced analytics, Pricing, Rocana, Splunk, 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: Data models and architecture, Database diversity, Databricks, Spark and BDAS, Intersystems and Cache', MOLAP, Object, Streaming and complex event processing (CEP)||3 Comments|
A lot of what I hear and talk about boils down to “data is a mess”. Below is a very partial list of examples.
To a first approximation, one would expect operational data to be rather clean. After all, it drives and/or records business transactions. So if something goes awry, the result can be lost money, disappointed customers, or worse, and those are outcomes to be strenuously avoided. Up to a point, that’s indeed true, at least at businesses large enough to be properly automated. (Unlike, for example — — mine.)
Even so, operational data has some canonical problems. First, it could be inaccurate; somebody can just misspell or otherwise botch an entry. Further, there are multiple ways data can be unreachable, typically because it’s:
- Inconsistent, in which case humans might not know how to look it up and database JOINs might fail.
- Unintegrated, in which case one application might not be able to use data that another happily maintains. (This is the classic data silo problem.)
Inconsistency can take multiple forms, including: Read more
Let’s start with some terminology biases:
- I dislike the term “big data” but like the Vs that define it — Volume, Velocity, Variety and Variability.
- Though I think it’s silly, I understand why BI innovators flee from the term “business intelligence” (they’re afraid of not sounding new).
So when my clients at Zoomdata told me that they’re in the business of providing “the fastest visual analytics for big data”, I understood their choice, but rolled my eyes anyway. And then I immediately started to check how their strategy actually plays against the “big data” Vs.
It turns out that:
- Zoomdata does its processing server-side, which allows for load-balancing and scale-out. Scale-out and claims of great query speed are relevant when data is of high volume.
- Zoomdata depends heavily on Spark.
- Zoomdata’s UI assumes data can be a mix of historical and streaming, and that if looking at streaming data you might want to also check history. This addresses velocity.
- Zoomdata assumes data can be in a variety of data stores, including:
- Relational (operational RDBMS, analytic RDBMS, or SQL-on-Hadoop).
- Files (generic HDFS — Hadoop Distributed File System or S3).*
- NoSQL (MongoDB and HBase were mentioned).
- Search (Elasticsearch was mentioned among others).
- Zoomdata also tries to detect data variability.
- Zoomdata is OEM/embedding-friendly.
*The HDFS/S3 aspect seems to be a major part of Zoomdata’s current story.
Core aspects of Zoomdata’s technical strategy include: Read more