Discussion of Hadoop. Related subjects include:
1. Continuing from last week’s HBase post, the Cloudera folks were fairly proud of HBase’s features for performance and scalability. Indeed, they suggested that use cases which were a good technical match for HBase were those that required fast random reads and writes with high concurrency and strict consistency. Some of the HBase architecture for query performance seems to be:
- Everything is stored in sorted files. (I didn’t probe as to what exactly the files were sorted on.)
- Files have indexes and optional Bloom filters.
- Files are marked with min/max field values and time stamp ranges, which helps with data skipping.
Notwithstanding that a couple of those features sound like they might help with analytic queries, the base expectation is that you’ll periodically massage your HBase data into a more analytically-oriented form. For example — I was talking with Cloudera after all — you could put it into Parquet.
2. The discussion of which kinds of data are originally put into HBase was a bit confusing.
- HBase is commonly used to receive machine-generated data. Everybody knows that.
- Cloudera drew a distinction between:
- Straightforward time series, which should probably just go into HDFS (Hadoop Distributed File System) rather than HBase.
- Data that is bucketed by entity, which likely should go into HBase. Examples of entities are specific users or devices.
- Cloudera also reminded me that OpenTSDB, a popular time series data store, runs over HBase.
OpenTSDB, by the way, likes to store detailed data and aggregates side-by-side, which resembles a pattern I discussed in my recent BI for NoSQL post.
3. HBase supports caching, tiered storage, and so on. Cloudera is pretty sure that it is publicly known (I presume from blog posts or conference talks) that: Read more
|Categories: Cloudera, eBay, Facebook, Hadoop, HBase, Market share and customer counts, NoSQL, Open source, Petabyte-scale data management, Specific users, Yahoo||4 Comments|
I talked with a couple of Cloudera folks about HBase last week. Let me frame things by saying:
- The closest thing to an HBase company, ala MongoDB/MongoDB or DataStax/Cassandra, is Cloudera.
- Cloudera still uses a figure of 20% of its customers being HBase-centric.
- HBaseCon and so on notwithstanding, that figure isn’t really reflected in Cloudera’s marketing efforts. Cloudera’s marketing commitment to HBase has never risen to nearly the level of MongoDB’s or DataStax’s push behind their respective core products.
- With Cloudera’s move to “zero/one/many” pricing, Cloudera salespeople have little incentive to push HBase hard to accounts other than HBase-first buyers.
- Cloudera no longer dominates HBase development, if it ever did.
- Cloudera is the single biggest contributor to HBase, by its count, but doesn’t make a majority of the contributions on its own.
- Cloudera sees Hortonworks as having become a strong HBase contributor.
- Intel is also a strong contributor, as are end user organizations such as Chinese telcos. Not coincidentally, Intel was a major Hadoop provider in China before the Intel/Cloudera deal.
- As far as Cloudera is concerned, HBase is just one data storage technology of several, focused on high-volume, high-concurrency, low-latency short-request processing. Cloudera thinks this is OK because of HBase’s strong integration with the rest of the Hadoop stack.
- Others who may be inclined to disagree are in several cases doing projects on top of HBase to extend its reach. (In particular, please see the discussion below about Apache Phoenix and Trafodion, both of which want to offer relational-like functionality.)
|Categories: Cloudera, Clustering, Data models and architecture, Database diversity, Hadoop, HBase, Hortonworks, HP and Neoview, Intel, Market share and customer counts, NoSQL, Open source||4 Comments|
- Continuuity toured in 2012 and touted its “app server for Hadoop” technology.
- Continuuity recently changed its name to Cask and went open source.
- Cask’s product is now called CDAP (Cask Data Application Platform). It’s still basically an app server for Hadoop and other “big data” — ouch do I hate that phrase — data stores.
- Cask and Cloudera partnered.
- I got a more technical Cask briefing this week.
- App servers are a notoriously amorphous technology. The focus of how they’re used can change greatly every couple of years.
- Partly for that reason, I was unimpressed by Continuuity’s original hype-filled positioning.
So far as I can tell:
- Cask’s current focus is to orchestrate job flows, with lots of data mappings.
- This is supposed to provide lots of developer benefits, for fairly obvious reasons. Those are pitched in terms of an integration story, more in a “free you from the mess of a many-part stack” sense than strictly in terms of data integration.
- CDAP already has a GUI to monitor what’s going on. A GUI to specify workflows is coming very soon.
- CDAP doesn’t consume a lot of cycles itself, and hence isn’t a real risk for unpleasant overhead, if “overhead” is narrowly defined. Rather, performance drags could come from …
- … sub-optimal choices in data mapping, database design or workflow composition.
I’m on record as believing that:
- Hadoop needs a memory-centric storage grid.
- Tachyon is a strong candidate to fill the role.
- It’s an open secret that there will be a Tachyon company. However, …
- … no details have been publicized. Indeed, the open secret itself is still officially secret.
- Tachyon technology, which just hit 0.6 a couple of days ago, still lacks many features I regard as essential.
- As a practical matter, most Tachyon interest to date has been associated with Spark. This makes perfect sense given Tachyon’s origin and initial technical focus.
- Tachyon was in 50 or more sites last year. Most of these sites were probably just experimenting with it. However …
- … there are production Tachyon clusters with >100 nodes.
As a reminder of Tachyon basics: Read more
|Categories: Clustering, Databricks, Spark and BDAS, Hadoop, Memory-centric data management||3 Comments|
7-10 years ago, I repeatedly argued the viewpoints:
- Relational DBMS were the right choice in most cases.
- Multiple kinds of relational DBMS were needed, optimized for different kinds of use case.
- There were a variety of specialized use cases in which non-relational data models were best.
Since then, however:
- Hadoop has flourished.
- NoSQL has flourished.
- Graph DBMS have matured somewhat.
- Much of the action has shifted to machine-generated data, of which there are many kinds.
So it’s probably best to revisit all that in a somewhat organized way.
While I don’t find the Open Data Platform thing very significant, an associated piece of news seems cooler — Pivotal is open sourcing a bunch of software, with Greenplum as the crown jewel. Notes on that start:
- Greenplum has been an on-again/off-again low-cost player since before its acquisition by EMC, but open source is basically a commitment to having low license cost be permanently on.
- In most regards, “free like beer” is what’s important here, not “free like speech”. I doubt non-Pivotal employees are going to do much hacking on the long-closed Greenplum code base.
- That said, Greenplum forked PostgreSQL a long time ago, and the general PostgreSQL community might gain ideas from some of the work Greenplum has done.
- The only other bit of newly open-sourced stuff I find interesting is HAWQ. Redis was already open source, and I’ve never been persuaded to care about GemFire.
Greenplum, let us recall, is a pretty decent MPP (Massively Parallel Processing) analytic RDBMS. Various aspects of it were oversold at various times, and I’ve never heard that they actually licked concurrency. But Greenplum has long had good SQL coverage and petabyte-scale deployments and a columnar option and some in-database analytics and so on; i.e., it’s legit. When somebody asks me about open source analytic RDBMS to consider, I expect Greenplum to consistently be on the short list.
Further, the low-cost alternatives for analytic RDBMS are adding up. Read more
|Categories: Amazon and its cloud, Citus Data, Data warehouse appliances, EAI, EII, ETL, ELT, ETLT, EMC, Greenplum, Hadoop, Infobright, MonetDB, Open source, Pricing||5 Comments|
Hortonworks, IBM, EMC Pivotal and others have announced a project called “Open Data Platform” to do … well, I’m not exactly sure what. Mainly, it sounds like:
- An attempt to minimize the importance of any technical advantages Cloudera or MapR might have.
- A face-saving way to admit that IBM’s and Pivotal’s insistence on having their own Hadoop distributions has been silly.
- An excuse for press releases.
- A source of an extra logo graphic to put on marketing slides.
Edit: Now there’s a press report saying explicitly that Hortonworks is taking over Pivotal’s Hadoop distro customers (which basically would mean taking over the support contracts and then working to migrate them to Hortonworks’ distro).
The claim is being made that this announcement solves some kind of problem about developing to multiple versions of the Hadoop platform, but to my knowledge that’s a problem rarely encountered in real life. When you already have a multi-enterprise open source community agreeing on APIs (Application Programming interfaces), what API inconsistency remains for a vendor consortium to painstakingly resolve?
Anyhow, it now seems clear that if you want to use a Hadoop distribution, there are three main choices:
- Cloudera’s flavor, whether as software (from Cloudera) or in an appliance (e.g. from Oracle).
- MapR’s flavor, as software from MapR.
- Hortonworks’ flavor, from a number of vendors, including Hortonworks, IBM, Pivotal, Teradata et al.
In saying that, I’m glossing over a few points, such as: Read more
|Categories: Amazon and its cloud, Cloudera, EMC, Emulation, transparency, portability, Greenplum, Hadoop, Hortonworks, IBM and DB2, MapR, Open source||10 Comments|
- 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||8 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.
There is much confusion about migration, by which I mean applications or investment being moved from one “platform” technology — hardware, operating system, DBMS, Hadoop, appliance, cluster, cloud, etc. — to another. Let’s sort some of that out. For starters:
- There are several fundamentally different kinds of “migration”.
- You can re-host an existing application.
- You can replace an existing application with another one that does similar (and hopefully also new) things. This new application may be on a different platform than the old one.
- You can build or buy a wholly new application.
- There’s also the inbetween case in which you extend an old application with significant new capabilities — which may not be well-suited for the existing platform.
- Motives for migration generally fall into a few buckets. The main ones are:
- You want to use a new app, and it only runs on certain platforms.
- The new platform may be cheaper to buy, rent or lease.
- The new platform may have lower operating costs in other ways, such as administration.
- Your employees may like the new platform’s “cool” aspect. (If the employee is sufficiently high-ranking, substitute “strategic” for “cool”.)
- Different apps may be much easier or harder to re-host. At two extremes:
- It can be forbiddingly difficult to re-host an OLTP (OnLine Transaction Processing) app that is heavily tuned, tightly integrated with your other apps, and built using your DBMS vendor’s proprietary stored-procedure language.
- It might be trivial to migrate a few long-running SQL queries to a new engine, and pretty easy to handle the data connectivity part of the move as well.
- Certain organizations, usually packaged software companies, design portability into their products from the get-go, with at least partial success.