Discussion of SQL-on-Hadoop and other forms of SQL/Hadoop integration.
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||20 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||2 Comments|
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
At the highest level:
- Presto is, roughly speaking, Facebook’s replacement for Hive, at least for queries that are supposed to run at interactive speeds.
- Teradata is announcing support for Presto with a classic open source pricing model.
- Presto will also become, roughly speaking, Teradata’s replacement for Hive.
- Teradata’s Presto efforts are being conducted by the former Hadapt.
Now let’s make that all a little more precise.
Regarding Presto (and I got most of this from Teradata)::
- To a first approximation, Presto is just another way to write SQL queries against HDFS (Hadoop Distributed File System). However …
- … Presto queries other data stores too, such as various kinds of RDBMS, and federates query results.
- Facebook at various points in time created both Hive and now Presto.
- Facebook started the Presto project in 2012 and now has 10 engineers on it.
- Teradata has named 16 engineers – all from Hadapt – who will be contributing to Presto.
- Known serious users of Presto include Facebook, Netflix, Groupon and Airbnb. Airbnb likes Presto well enough to have 1/3 of its employees using it, via an Airbnb-developed tool called Airpal.
- Facebook is known to have a cluster cited at 300 petabytes and 4000 users where Presto is presumed to be a principal part of the workload.
Daniel Abadi said that Presto satisfies what he sees as some core architectural requirements for a modern parallel analytic RDBMS project: Read more
I have a small blacklist of companies I won’t talk with because of their particularly unethical past behavior. Actian is one such; they evidently made stuff up about me that Josh Berkus gullibly posted for them, and I don’t want to have conversations that could be dishonestly used against me.
That said, Peter Boncz isn’t exactly an Actian employee. Rather, he’s the professor who supervised Marcin Zukowski’s PhD thesis that became Vectorwise, and I chatted with Peter by Skype while he was at home in Amsterdam. I believe his assurances that no Actian personnel sat in on the call.
In other news, Peter is currently working on and optimistic about HyPer. But we literally spent less than a minute talking about that
Before I get to the substance, there’s been a lot of renaming at Actian. To quote Andrew Brust,
… the ParAccel, Pervasive and Vectorwise technologies are being unified under the Actian Analytics Platform brand. Specifically, the ParAccel technology … is being re-branded Actian Matrix; Pervasive’s technologies are rechristened Actian DataFlow and Actian DataConnect; and Vectorwise becomes Actian Vector.
Actian … is now “one company, with one voice and one platform” according to its John Santaferraro
The bolded part of the latter quote is untrue — at least in the ordinary sense of the word “one” — but the rest can presumably be taken as company gospel.
All this is by way of preamble to saying that Peter reached out to me about Actian’s new Vector Hadoop Edition when he blogged about it last June, and we finally talked this week. Highlights include: Read more
|Categories: Actian and Ingres, Clustering, Database compression, Hadoop, ParAccel, Pervasive Software, SQL/Hadoop integration, VectorWise, Workload management||4 Comments|
My client Teradata bought my (former) clients Revelytix and Hadapt.* Obviously, I’m in confidentiality up to my eyeballs. That said — Teradata truly doesn’t know what it’s going to do with those acquisitions yet. Indeed, the acquisitions are too new for Teradata to have fully reviewed the code and so on, let alone made strategic decisions informed by that review. So while this is just a guess, I conjecture Teradata won’t say anything concrete until at least September, although I do expect some kind of stated direction in time for its October user conference.
*I love my business, but it does have one distressing aspect, namely the combination of subscription pricing and customer churn. When your customers transform really quickly, or even go out of existence, so sometimes does their reliance on you.
I’ve written extensively about Hadapt, but to review:
- The HadoopDB project was started by Dan Abadi and two grad students.
- HadoopDB tied a bunch of PostgreSQL instances together with Hadoop MapReduce. Lab benchmarks suggested it was more performant than the coyly named DBx (where x=2), but not necessarily competitive with top analytic RDBMS.
- Hadapt was formed to commercialize HadoopDB.
- After some fits and starts, Hadapt was a Cambridge-based company. Former Vertica CEO Chris Lynch invested even before he was a VC, and became an active chairman. Not coincidentally, Hadapt had a bunch of Vertica folks.
- Hadapt decided to stick with row-based PostgreSQL, Dan Abadi’s previous columnar enthusiasm notwithstanding. Not coincidentally, Hadapt’s performance never blew anyone away.
- Especially after the announcement of Cloudera Impala, Hadapt’s SQL-on-Hadoop positioning didn’t work out. Indeed, Hadapt laid off most or all of its sales and marketing folks. Hadapt pivoted to emphasize its schema-on-need story.
- Chris Lynch, who generally seems to think that IT vendors are created to be sold, shopped Hadapt aggressively.
As for what Teradata should do with Hadapt: Read more
|Categories: Aster Data, Citus Data, Cloudera, Columnar database management, Data warehousing, Hadapt, Hadoop, MapReduce, Oracle, SQL/Hadoop integration, Teradata||7 Comments|
Oracle is announcing today what it’s calling “Oracle Big Data SQL”. As usual, I haven’t been briefed, but highlights seem to include:
- Oracle Big Data SQL is basically data federation using the External Tables capability of the Oracle DBMS.
- Unlike independent products — e.g. Cirro — Oracle Big Data SQL federates SQL queries only across Oracle offerings, such as the Oracle DBMS, the Oracle NoSQL offering, or Oracle’s Cloudera-based Hadoop appliance.
- Also unlike independent products, Oracle Big Data SQL is claimed to be compatible with Oracle’s usual security model and SQL dialect.
- At least when it talks to Hadoop, Oracle Big Data SQL exploits predicate pushdown to reduce network traffic.
And by the way – Oracle Big Data SQL is NOT “SQL-on-Hadoop” as that term is commonly construed, unless the complete Oracle DBMS is running on every node of a Hadoop cluster.
Predicate pushdown is actually a simple concept:
- If you issue a query in one place to run against a lot of data that’s in another place, you could spawn a lot of network traffic, which could be slow and costly. However …
- … if you can “push down” parts of the query to where the data is stored, and thus filter out most of the data, then you can greatly reduce network traffic.
“Predicate pushdown” gets its name from the fact that portions of SQL statements, specifically ones that filter data, are properly referred to as predicates. They earn that name because predicates in mathematical logic and clauses in SQL are the same kind of thing — statements that, upon evaluation, can be TRUE or FALSE for different values of variables or data.
The most famous example of predicate pushdown is Oracle Exadata, with the story there being:
- Oracle’s shared-everything architecture created a huge I/O bottleneck when querying large amounts of data, making Oracle inappropriate for very large data warehouses.
- Oracle Exadata added a second tier of servers each tied to a subset of the overall storage; certain predicates are pushed down to that tier.
- The I/O between Exadata’s two sets of servers is now tolerable, and so Oracle is now often competitive in the high-end data warehousing market,
Oracle evidently calls this “SmartScan”, and says Oracle Big Data SQL does something similar with predicate pushdown into Hadoop.
Oracle also hints at using predicate pushdown to do non-tabular operations on the non-relational systems, rather than shoehorning operations on multi-structured data into the Oracle DBMS, but my details on that are sparse.
- Chris Kanaracus’ coverage of the announcement quotes me at length.
|Categories: Data warehousing, Exadata, Hadoop, Oracle, SQL/Hadoop integration, Theory and architecture||8 Comments|
I’m commonly asked to assess vendor claims of the kind:
- “Our system lets you do multiple kinds of processing against one database.”
- “Otherwise you’d need two or more data managers to get the job done, which would be a catastrophe of unthinkable proportion.”
So I thought it might be useful to quickly review some of the many ways organizations put multiple data stores to work. As usual, my bottom line is:
- The most extreme vendor marketing claims are false.
- There are many different choices that make sense in at least some use cases each.
Horses for courses
It’s now widely accepted that different data managers are better for different use cases, based on distinctions such as:
- Short-request vs. analytic.
- SQL vs. non-SQL (NoSQL or otherwise).
- Expensive/heavy-duty vs. cheap/easy-to-support.
Vendors are part of this consensus; already in 2005 I observed
For all practical purposes, there are no DBMS vendors left advocating single-server strategies.
Vendor agreement has become even stronger in the interim, as evidenced by Oracle/MySQL, IBM/Netezza, Oracle’s NoSQL dabblings, and various companies’ Hadoop offerings.
Multiple data stores for a single application
We commonly think of one data manager managing one or more databases, each in support of one or more applications. But the other way around works too; it’s normal for a single application to invoke multiple data stores. Indeed, all but the strictest relational bigots would likely agree: Read more
One of my lesser-known clients is Citus Data, a largely Turkish company that is however headquartered in San Francisco. They make CitusDB, which puts a scale-out layer over a collection of fully-functional PostgreSQL nodes, much like Greenplum and Aster Data before it. However, in contrast to those and other Postgres-based analytic MPP (Massively Parallel Processing) DBMS:
- CitusDB does not permanently fork PostgreSQL; Citus Data has committed to always working with the latest PostgreSQL release, or at least with one that’s less than a year old.
- Citus Data never made the “fat head” mistake — if a join can’t be executed directly on the CitusDB data-storing nodes, it can’t be executed in CitusDB at all.
- CitusDB follows the modern best-practice of having many virtual nodes on each physical node. Default size of a virtual node is one gigabyte. Each virtual node is technically its own PostgreSQL table.*
- Citus Data has already introduced an open source column-store option for PostgreSQL, which CitusDB of course exploits.
*One benefit to this strategy, besides the usual elasticity and recovery stuff, is that while PostgreSQL may be single-core for any given query, a CitusDB query can use multiple cores by virtue of hitting multiple PostgreSQL tables on each node.
Citus has thrown a few things against the wall; for example, there are two versions of its product, one which involves HDFS (Hadoop Distributed File System) and one of which doesn’t. But I think Citus’ focus will be scale-out PostgreSQL for at least the medium-term future. Citus does have actual customers, and they weren’t all PostgreSQL users previously. Still, the main hope — at least until the product is more built-out — is that existing PostgreSQL users will find CitusDB easy to adopt, in technology and price alike.
|Categories: Aster Data, Citus Data, Columnar database management, Data warehousing, Database compression, Greenplum, Hadoop, Parallelization, PostgreSQL, SQL/Hadoop integration, Transparent sharding, Workload management||6 Comments|
There’s much confusion about Cloudera’s SQL plans and beliefs, and the company has mainly itself to blame. That said, here’s what I think is going on.
- Hive is good at some tasks and terrible at others.
- Hive is good at batch data transformation.
- Hive is bad at ad-hoc query, unless you really, really need Hive’s scale and low license cost. One example, per Eli Collins: Facebook has a 500 petabyte Hive warehouse, but jokes that on a good day an analyst can run 6 queries against it.
- Impala is meant to be good at what Hive is bad at – i.e., fast-response query. (Cloudera mentioned reliable 100 millisecond response times for at least one user.)
- Impala is also meant to be good at what Hive is good at, and will someday from Cloudera’s standpoint completely supersede Hive, but Cloudera is in no hurry for that day to arrive. Hive is more mature. Hive still has more SQL coverage than Impala. There’s a lot of legacy investment in Hive. Cloudera gets little business advantage if a customer sunsets Hive.
- Impala is already decent at some tasks analytic RDBMS are commonly used for. Cloudera insists that some queries run very quickly on Impala. I believe them.
- Impala is terrible at others, including some of the ones most closely associated with the concept of “data warehousing”. Data modeling is a big zero right now. Impala’s workload management, concurrency and all that are very immature.
- There are some use cases for which SQL-on-Hadoop blows away analytic RDBMS, for example ones involving data transformations – perhaps on multi-structured data – that are impractical in RDBMS.
And of course, as vendors so often do, Cloudera generally overrates both the relative maturity of Impala and the relative importance of the use cases in which its offerings – Impala or otherwise – shine.
- A survey of SQL/Hadoop integration (February, 2014)
- The cardinal rules of DBMS development (March, 2013)
|Categories: Cloudera, Data warehousing, Facebook, Hadoop, SQL/Hadoop integration, Workload management||4 Comments|