## ByteArrayOutputStream is really, really slow sometimes in JDK6

TLDR: Yesterday I mentioned on Twitter that I’d found a bad performance problem when writing to a large ByteArrayOutputStream in Java. After some digging, it appears to be the case that there’s a bad bug in JDK6 that doesn’t affect correctness, but does cause performance to nosedive when a ByteArrayOutputStream gets large. This post explains why.

## On Raft, briefly

Raft is a new-ish consensus implementation whose great benefit, to my mind it, is its applicability for real systems. We briefly discussed it internally at Cloudera, and I thought I’d share what I contributed, below. There’s an underlying theme here regarding the role of distributed systems research in practitioners’ daily work, and how the act of building a distributed system has not yet been sufficiently well commoditised to render a familiarity with the original research unnecessary. [Read More]

## Some miscellanea

CAP FAQ I wrote an FAQ on The CAP Theorem. The aim is to definitively settle some of the common misconceptions around CAP so as to help prevent its invocation in useless places. If someone says they got around CAP, refer them to the FAQ. It should be a pretty simple introduction to the theorem as well. I think that CAP itself is a pretty uninteresting result, but it does at least shine a light on tradeoffs implicit in distributed systems. [Read More]

## Columnar Storage

You’re going to hear a lot about columnar storage formats in the next few months, as a variety of distributed execution engines are beginning to consider them for their IO efficiency, and the optimisations that they open up for query execution. In this post, I’ll explain why we care so much about IO efficiency and show how columnar storage - which is a simple idea - can drastically improve performance for certain workloads.

Caveat: This is a personal, general research summary post, and as usual doesn’t neccessarily reflect our thinking at Cloudera about columnar storage.

Disks are still the major bottleneck in query execution over large datasets. Even a machine with twelve disks running in parallel (for an aggregate bandwidth of north of 1GB/s) can’t keep all the cores busy; running a query against memory-cached data can get tens of GB/s of throughput. IO bandwidth matters. Therefore, the best thing an engineer can do to improve the performance of disk-based query engines (like RDBMs and Impala) usually is to improve the performance of reading bytes from disk. This can mean decreasing the latency (for small queries where the time to find the data to read might dominate), but most usually this means improving the effective throughput of reads from disk.

The traditional way to improve disk bandwidth has been to wait, and allow disks to get faster. However, disks are not getting faster very quickly (having settled at roughly 100 MB/s, with ~12 disks per server), and SSDs can’t yet achieve the storage density to be directly competitive with HDDs on a per-server basis.

The other way to improve disk performance is to maximise the ratio of ‘useful’ bytes read to total bytes read. The idea is not to read more data than is absolutely necessary to serve a query, so the useful bandwidth realised is increased without actually improving the performance of the IO subsystem. Enter columnar storage, a principle for file format design that aims to do exactly that for query engines that deal with record-based data.

## Cloudera Impala

If you have a strong background in either databases or distributed systems, and fancy working on such an exciting technology, send me a note!

It’s great to finally be able to say something about what I’ve been working at Cloudera for nearly a year. At StrataConf / Hadoop World in New York a couple of weeks ago we announced Cloudera Impala. Impala is a distributed query execution engine that understands a subset of SQL, and critically runs over HDFS and HBase as storage managers. It’s very similar in functionality to Apache Hive, but it is much, much, much (anecdotally up to 100x) faster.

## On some subtleties of Paxos

There’s one particular aspect of the Paxos protocol that gives readers of this blog - and for some time, me! - some difficulty. This short post tries to clear up some confusion on a part of the protocol that is poorly explained in pretty much every major description.

Reasoning about Knowledge Toward a Cloud Computing Research Agenda (2009) - “One of the LADIS attendees commented at some point that Byzantine Consensus could be used to improve Chubby, making it tolerant of faults that could disrupt it as currently implemented. But for our keynote speakers, enhancing Chubby to tolerate such faults turns out to be of purely academic interest.” Low-level data structures - > The llds general working thesis is: for large memory applications, virtual memory layers can hurt application performance due to increased memory latency when dealing with large data structures. [Read More]

## Something a bit different: translations of classic mathematical texts (!)

During his retirement, my father has been able to spend much time indulging his love of mathematics. This included, amongst other impressive endeavours, attending Cambridge at a more advanced age than average to take (and pass!) the Part III of the Mathematical Tripos, often considered one of the hardest taught courses in maths in the world. Since then, he has hardly been idle, and has recently been undertaking a translation of a classic work in modern algebra by Dedekind and Weber from its original 100+ pages of German into English. [Read More]

## EuroSys 2012 blog notes

EuroSys 2012 was last week - one of the premier European systems conferences. Over at the Cambridge System Research Group’s blog, various people from the group have written notes on the papers presented. They’re very well-written summaries, and worth checking out for an overview of the research presented.

## FLP and CAP aren't the same thing

An interesting question came up on Quora this last week. Roughly speaking, the question asked how, if at all, the FLP theorem and the CAP theorem were related. I’d thought idly about exactly the same question myself before. Both theorems concern the impossibility of solving fairly similar fundamental distributed systems problems in what appear to be fairly similar distributed systems settings. The CAP theorem gets all the airtime, but FLP to me is a more beautiful result. Wouldn’t it be fascinating if both theorems turned out to be equivalent; that is effectively restatements of each other?