Hadoop 2: shipping!

The week-long vote is in- Hadoop 2 is now officially released by Apache!

Anyone who wants to use this release should download Hadoop via the Apache Mirrors.

Maven and Ivy users: the version you want to refer to is 2.2.0

The artifacts haven't trickled through to the public repos as of 2013-10-16-11:18 GMT -they are on the ASF staging repo and I've been using them happily all week.

This release marks an epic of development, with YARN being a fundamental rethink of what you can run in a Hadoop cluster: anything you can get to run in a distributed cluster where failures will happen, the Hadoop FileSystem the API for filesystem access  -be it in Java or a native client- and data is measured by the Petabyte.

YARN is going to get a lot of press for the way it transforms what you can do in the cluster, but HDFS itself has changed a lot. This is the first ASF release with active/passive HA -which is why Zookeeper is now on the classpath. CDH 4.x shipped with an earlier version of this- and as we haven't heard of any dramatic data loss events, consider it well tested in the field. Admittedly, if you misconfigure things failover may not happen, but that's something you can qualify with a kill -9 of the active namenode service. Do remember to have to >1 zookeeper instance before you try this -testing ZK failure should also be a qualification process. I think 5 is a number considered safer than 3, though I've heard of one cluster running with 9. Nobody has admitted going up to 11.

This release also adds to HDFS

1. NFS support: you can mount the FS as an NFS v3 filesystem. This doesn't give you the ability to write to anywhere other than the tail of a file -HFDS is still not-Posix. But then neither is NFS: its caching means that there is a few seconds worth of eventual consistency across nodes (\cite{Distributed Systems, Colouris, Dollimore & Kindberg, p331}).,
2. Snapshots: you can snapshot some of a filesystem and roll back to it later. Judging by the JIRAs, quotas get quite complex there. What it does mean is that it is harder to lose data by accidental rm -rf operations.
3. HDFS federation: datanodes can store data for different HDFS namenodes, -Block Storage is now a service- while clients can mount different HDFS filesystems to get access to the data. This is something of primarily of relevance to people working at Yahoo! and facebook scale -everyone else can just get more RAM for their NN and tune the GC options to not lock the server too much]

Hadoop 2 also adds is extensive testing all the way up the stack. In particular, there's a new HBase release coming out soon -hopefully HBase 0.96 will be out in days. Lots of other things have been tested against it -which has helped to identify any incompatibilities between the Hadoop 1.x MapReduce API (MRv1) and Hadoop 2's MRv2, while also getting patches into the the rest of the stack where appropriate. As new releases trickle out, everything will end up being built and qualified on Hadoop 2.

Which is why when you look at the features in Hadoop 2.x, as well as headline items "YARN, HDFS Snapshots, ...", you should also consider the testing and QA that went into this -this is the first stable Hadoop 2 release -the first one extensively tested all the way up the stack. Which is why everyone doing that QA -my colleagues, Matt Foley's QA team, the Bigtop developers, and anyone else working with Hadoop that built and tested their code against Hadoop 2.1 beta and later RCs -and reported bugs.

QA teams: your work is appreciated! Take the rest of the week off!

[photo: Deamz and Soker at St Philips, near Old Market]

Attack Surface

I allege that

1. Any program that is capable of parsing untrusted content is vulnerable to exploitation by malicious entities.
   
2. Programs which parse binary file contents are particularly vulnerable due to the tendency of such formats to use offset pointers and other values which the parsers assume are valid -but which "fuzzing" can be used to discover new exploits.
   
3. Programs that treat the data as containing executable content -even within a sandbox- are vulnerable to any exploit that permits code to escape the sandbox, or simply to Denial of Service attacks in which resources in the sandbox such as memory, CPU time and network bandwidth are consumed.
   
4. Recent incidents involving the generation of false certificates for popular web sites have shown that signed web sites and signed content cannot be consistently trusted.
   
5. Any program that can process content downloaded by an HTTP client application is capable of parsing untrusted content, and so vulnerable to exploitation.
   
6. Any program that can process content received in the payload of email messages is capable of parsing untrusted content, and so vulnerable to exploitation.
   
7. Any program that can process delivered in untrusted physical media is capable of parsing untrusted content, and so vulnerable to exploitation.
   

The last of these items concerns me today. The Stuxnet worm has shown how a well-engineered piece of malware can propagate between windows machines, and across an airgap from windows machines to SCADA infrastructure by way of USB infection. The Sony Rootkit Scandal showed that even large business were willing to use this to sneak on anti-copying features on what pretended to be a music CD.

A key conclusion from the CERT advisory on the Sony Incident was Disable automatically running CD-ROMs by editing the registry to change the Autorun value to 0 (zero) as described in Microsoft Article 155217.


I've long disabled autorun on windows boxes, and I also remember testing a long time ago whether or not you could autorun a CD while the screen was locked. Answer: no. Windows waits until the screen is unlocked before an autorun application is executed. This provides some defence against malicious CDs, though not as much as disabling autorun completely.


I don't worry too much about windows security these days because my main machines are Linux boxes, which keep themselves up to date with apt-get update. This keeps browsers, flash, JVMs, OpenOffice current, which reduces their vulnerability. I also keep an eye on the SANS newsfeed to see if there are any 0-day exploits out, and take proactive action if anything gets out into the wild that we are at risk from. I've stopped fielding service requests from family members saying "there is something on my machine -can you clean it up". If I am asked, my response is "I will reformat your HDD and install ubuntu on it". FWIW, I have done this for one relative and it works really well.


I am confident that I do keep my machines locked down, and know that the primary vulnerability on an Linux system on which I do open source development is that I implicitly have to trust everyone who has commit rights to every application that I use on it.


If there is one issue I have with Linux is that it is over-paranoid. For example, once the screen is locked I can't suspend the system. This means that I can't suspend the laptop until I unlock it and hit the suspend button. Somewhere on launchpad there was a bugrep about that, but people who looked after servers were saying "no!", despite the fact that if I had physical access to the power button, it would be only four-seconds away from a hard power off, and then, unless the system had the CD-ROM, USB and PXE boot disabled, one to two minutes from being my box. It's annoying but a sign that security is taken over-seriously.


Imagine my surprise then, last week, on inserting a CD-ROM from an external source when suddenly the system started grinding away, with a tiny thumbnail of each PDF image appearing one after the other. For that to happen the desktop had to have scanned through the list of files, handed each PDF file off to some parser and got a 64x64 graphic back. It wasn't the wasted effort that concerned me -it was the way it did this without me even asking.


I did some research and discovered an article on the topic. Essentially the Linux desktop standard allows for autostart.sh files to be executed on mounting a media device. The the thumbnailing is something else: nautilus defaults to doing this for local files -and views all files on mounted local media as "local", rather than "untrusted content on an untrusted filesystem". Given that exploits have been found in the thumbnailing code, having Nautilus thumbnail files on an external device is insane.


The best bit: this happens even while the desktop is locked.


There I am, regularly saying rude words because I thought I'd suspended my laptop, but as the screen was locked it's just stayed powered up, slowly killing the battery and/or overheading in a bag. I accepted this inconvenience as I thought it was a security feature for my own good. It turns out that it was irrelevant as all you needed to do was plug in a USB stick with a any .dvi, .png or .pdf file designed to exploit some security hole and my box is 0wned. Which, if done while I am logged in, offers access to the encrypted bits of the box.

There is a workaround -disable all thumbnailing even of local content, as well as the usual "do nothing" action on handling media insertions. I've done that everywhere. Yet what annoys me is this: what were the dev team thinking. On the one hand there is the power management group saying "we won't let people suspend while the desktop is locked for security reasons", while some other people are thinking "let's generate thumbnails whenever new media is inserted". The strength of OSS is that anyone can contribute. The weakness: a lot of them aren't devious enough to recognise what they think of a feature is someone else's route of access to your systems


[Photo: Park Street]

Solving the Netapp Open Solution for Hadoop Solutions Guide

I have been staring at the Netapp Open Solution for Hadoop Solutions Guide. I now have a headache.

Where do I begin? It's interesting to read a datasheet about Hadoop that is targeted at enterprise filesystem people. Not management "Hadoop solves all your problems", not developer "you have to rewrite all your apps", but something designed to convince people who worry about file systems that HDFS is a wrongness on the face of the planet. That's the only reason it would make sense. And like anyone trying to position a competing product to HDFS they have to completely discredit HDFS.

This paper, which tries to rejustify splitting the storage and compute platforms -the whole thing that the HDFS/Hadoop is designed to eliminate on cost grounds- has to try pretty hard.

In fact, I will say that after reading this paper that the MapR/EMC story makes a lot more sense. As what the Netapp paper is trying to say -unlike EMC-  is "the Big Data platform that Hadoop is wonderful, provided you ignore the cost model of SATA-on-server that HDFS and equivalent filesystems offer for topology-aware applications".

This must have been a hard article to write, so I must give the marketing team some credit for the attempt, even if they got it so wrong technically.


First: did we really need new acronyms? Really? By Page 4 there is a new one, "REPCOUNT", that I have never come across before. Then the acronym "HDFS3" that I first thought meant version 3 of HDFS, but before I could switch to JIRA and see if someone had closed an issue "design, implement and release V3 of HDFS", I see it really means "A file saved to HDFS with the block.replication.factor attribute set to 3", or more simply, 3x replicated blocks. No need to add another acronym to a document that is knee deep in them.


Now, for some more serious criticisms, starting at page 5: 

"For each 1 petabyte of data, 3 terabytes of raw capacity are needed".

If that were true, this would not be a criticism. No, it would be a sign of someone getting such a fantastic compression ration that Facebooks leading edge 30 PB server would fit into 30x3TB LFF HDDs, which you could fit into about 3U's worth of rack. If only. And don't forget that RAID uses 1.6X raw storage: it replicates too, just more efficiently.

Then there's the ingest problem. A whole section of this paper is dedicated to scaring people that they won't be able to pull in data fast enough because the network traffic will overload the system. We are not -outside my home network- using Ether over Power as the backplane any more. You can now get 46 servers with 12x3TB HDDs in them onto a single rack, with a 10GbE switch that run "with all the lights on". On a rack like that - which offers 500+TB of storage- you can sustain 10 GbE between any two servers. If the ingress and egress servers are hooked up to the same switch you could in theory move a Gigabyte per second between any two nodes or in and out the cluster. Nobody has an ingress right in that range, except maybe the physicists and their "odd" experiments. Everyone else can predict their ingress rate fairly simply, it is "look around at the amount of data you discard today". That's your ingress rate. Even a terabyte per day is pretty significant -yet on a 10GbE switched you could possibly import that from a single in-centre-off-HDFS-node in under 20 minutes. For anything out of the datacentre, your bandwidth will probably be less than 10 Gigabits, unless your name is Facebook, Yahoo!, Google or Microsoft.


Summary: ingress rate is unlikely to be bottlenecked by in-rack bandwidth even with 3x replication.

Next: Intra-HDFS bandwidth issues.

The paper says today that "A typical Hadoop node has 2GbE interfaces". No. A typical node tends to have a single 1GbE interface; bonded 2x1 GbE is rarer, as it's harder to commission.
Go that way and add on separate ToR switches and all concerns about "network switch failure" go away. You'd have to lose two network switches or power to the entire rack to trigger re-replication. At least NetApp did notice that 10GbE is going on the mainboard and said "Customers building modest clusters (of less than 128 nodes) with modern servers should consider 10GbaseT network topologies."

I'd say something different, which is "128 nodes is not a modest cluster". If you are building your rack from paired six-core CPUs with 12 LFF HDDs each with 3TB of SATA (how's that for acronyms?), then your cluster has 1536 individual cores. It will have 12*3*128 = 4608TB of storage: four petabytes. That's is not modest. That is something that can sort a petabyte of data with. Fit that up with a good 10GbE switch and enough RAM -and the latest 0.23 alpha release with Todd's CRC32c-in-SSE patch- and you could probably announce you have just won the petabyte sorting record.

Summary: "a 128 node cluster built of current CPUs and SATA storage is not modest".

It's a modest size -three racks- but it will probably be the largest file system your organisation owns. Think about that. And think about the fact that when people say "big data" what they mean is "lots of low value data". If you don't store all of it (aggressively compressed), you will regret it later.

Page 6: Drive failures. This  page and its successors wound me up so much I had to stop. You see, I've been slowly doing some changes to trunk and 0.23 to tune HDFS's failure handling here, based on papers on failures by Google, Microsoft and -um- Netapp. This means I am starting to know more about the subject, and defend my statements.

The Netapp paper discusses the consequences of HDD Failure rates using the failure rate on 5K-node cluster to scare people. Yes, there will be a high failure rate there, but it's a background noise. The latest version of Hadoop 0.20.20x doesn't  require a DataNode restart when a disk fails -take it away and only that 1-3 TB of data fails. When a disk fails -and this is where whoever wrote the paper really misses the point - that 2TB of missing data is still scattered across all other clusters in the rack.

If you have 128 servers in your "modest" cluster, 2TB disks and a block size of 256MB, then there were about 8000 blocks in the disk, which are now scattered across (128-1) servers. Sixtyish blocks per server. With twelve disks per server, that's about five blocks per SATA disk (=2500 MB). Even if -as claimed- the throughput of a single SATA disk is 30MB/s, those five blocks will take under two minutes to be read off disk. I'm not going to follow this chain through to network traffic as you'd also have to factor in the fact that servers are reading network packets from blocks being replicated to it at the same time and saving them to disk too (it should expect 60 blocks incoming), but the key point is this: on a 128 node cluster, the loss of a single 2TB cluster will generate a blip of network traffic, and your cluster will carry on as normal. No need for "24-hour staffing of data center technicians". No need for the ops teams to even get texted when a disk fails. Yes, loss of a full 36TB server is a bit more dramatic, but with 10 GbE it's still manageable. This paper is just trying to scare you based on a failure to understand how block replication is implicit striping of a file across the entire cluster, and they haven't looked at Hadoop's source to see what it does on a failure. Hint: look at BlockManager.java. I have.


To summarise:


The infrastructure handles failures, the apps are designed for it. This is not your old enterprise infrastructure. We have moved out of the enterprise world of caring about every disk that fails, and into a world of statistics. 


I'm going to stop here. I can't be bothered to read the low level details if the high level stuff is so painfully wrong, either through inadequate technical review of the marketing team's world view, or deliberately through a failure to understand HDFS.

[Update 21:57 GMT]. Actually it is weirder than I first thought. This is still HDFS, just running on more expensive hardware. You get the (current) HDFS limitations: no native filesystem mounting, a namenode to care about, security on a par with NFS, without the cost savings of pure-SATA-no-licensing-fees. Instead you have to use RAID everywhere, which not only bumps up your cost of storage, puts you at risk of RAID controller failure and errors in the OS drivers for those controller (hence their strict rules about which Linux releases to trust). If you do follow their recommendations and rely on hardware for data integrity, you've cut down the probability of node-local job execution, so all FUD about replication traffic is now moot as at least 1/3 more of your tasks will be running remote -possibly even with the Fair Scheduler, which waits for a bit to see if a local slot becomes free. What they are doing then is adding some HA hardware underneath a filesystem that is designed to give strong availability out of medium availability hardware. I have seen such a design before, and thought it sucked then too.  Information week says this is a response to EMC, but it looks more like NetApp's strategy to stay relevant, and Cloudera are partnering with them as NetApp offered them money and if it sells into more "enterprise customers" then why not? With the extra hardware costs of NetApp the cloudera licenses will look better value, and clearly both NetApp and their customers are in need of the hand-holding that Cloudera can offer.

I just wish someone from Cloudera had reviewed that solutions paper for technical validity before it got published.




[Artwork: ARYZ, Nelson Street]

Hadoop work in the new API in Groovy.

I've been doing some actual Map/Reduce work with the new API, to see how it's changed. One issue: not enough documentation. Here then is some more, and different in a very special way: the code is in Groovy.

To use them: get the groovy-all JAR on your Hadoop classpath and use the groovyc compiler to compile your groovy source (and any java source nearby) into your JAR, bring up your cluster and submit the work like anything else.

This pair of operations, part of a test to see how well Groovy MR jobs work just counts the lines in a source file; about as simple as you can get.

The mapper:

package org.apache.example.groovymr

import org.apache.hadoop.io.IntWritable
import org.apache.hadoop.io.Text
import org.apache.hadoop.io.LongWritable
import org.apache.hadoop.mapreduce.Mapper

class GroovyLineCountMapper extends Mapper {

    final static def emitKey = new Text("lines")
    final static def one = new IntWritable(1)

    void map(LongWritable key,
             Text value,
             Mapper.Context context) {
        context.write(emitKey, one)
    }
}

Nice and simple; little different from the Java version except

• Semicolons are optional.
• The line ending rules are stricter to compensate, hence lines end with a comma or other half-finished operation.
• You don't have to be so explicit about type (the def declarations) -and let the runtime sort it out. I have mixed feelings about that.

There's one other quirk in that the Context parameter for the map operation (which is a generic type of the parent class) has to be explicitly declared as Mapper.Context. I have no idea why, except that it won't compile otherwise. The same goes for the Reducer.Context

Not much to see there then. What is more interesting is the reduction side of things.

package org.apache.example.groovymr
import org.apache.hadoop.io.IntWritable
import org.apache.hadoop.io.Text
import org.apache.hadoop.mapreduce.Reducer

class GroovyValueCountReducer 
        extends Reducer {

    void reduce(Text key,
                Iterable values,
                Reducer.Context context) {
        int sum = values.collect() { it.get() } .sum()
        context.write(key, new IntWritable(sum));
    }
}

See the line in bold? It's taking the iterable of the list of values for that key, applying a closure to them (getting the values), which returns a list of type integer, which is then all summed up. That is: there is a per-element transform (it.get()) and a merging of all the results (sum()). Which is. when you think about it, almost a Map and a Reduce.

It's turtles all the way down.

[Artwork: Banksy, 2009 Bristol exhibition]

Microsoft and Hadoop: interesting

I am probably expected to say negative things about the recent announcement of Microsoft supporting Apache Hadoop(tm) on their Azure Cloud Platform, but I won't. I am impressed and think that it is a good thing.

1. It shows that the Hadoop-ecosystem is becoming more ubiquitous. Yes it has flaws, which I know as one of my IntelliJ IDEA instances has the 0.24 trunk open in a window and I am staring at the monitoring2 code thinking "why didn't they use ByteBuffer here instead of hacking together XDR records by hand" (FWIW, Todd "ganglia" Lipcon says it aint his code). Despite these flaws, it is becoming widespread,
2. That helps the layers on top, the tools that work to the APIs, the applications.
3. This gives the Hadoop ecosystem more momentum and stops alternatives getting a foothold. In particular the LexisNexis stuff -from a company that talk about "Killing Hadoop". Got some bad news there...
4. Microsoft have promised contribute stuff back. This is more than Amazon have ever done -yet AWS must have found truckloads of problems. Everyone else does: and we file bugs, then try to fix them. I could pick any mildly-complex source file in the tree, do a line-by-line code review and find something to fix, even if its just better logging or error handling. (don't dismiss error handling BTW
5. If Amazon have forked, they get to keep that fork up to date.
6. If MS do contribute stuff back, it will make Hadoop work properly under Windows. For now you have to install Cygwin because Hadoop calls out to various unix commands a lot of the time. A windows-specific library for these operations will make Hadoop not only more useful in Windows clusters, it will make it better for developers.
7. MS will test at scale on Windows, which will find new bugs, bugs that they and Hortonworks will fix. Ideally they will add more functional tests too.
8. I get to say to @savasp that my code is running in their datacentre. Savas: mine is the networking stuff to get it to degrade better on a badly configured home network.  Your ops team should not encounter this.

It's interesting that Microsoft have done this. Why?

• It could be indicative of a low takeup of Azure outside the MS enterprise community. I used to do a lot of win32 programming (in fact I once coded on windows/386 2.04); I don't miss it, even though Visual Studio 6 used to be a really good C++ IDE. It is nicer to live in the Unix land that Kernighan and Ritchie created.(*)
• Any data mining tooling encourages you to keep data, which earns money for all cloud service providers.
• The layers on top are becoming interesting. That's the extra code layers, the GUI integration, etc.
• There's no reason why enterprise customers can't also run Hadoop on windows server within their own organisations, so integrate with the rest of their world. (I'm ignoring cost of the OS here, because if you pay for RHEL6 and CDH then the OS costs become noise).
• If you are trying to run windows code as part of your MR or Pig jobs, you now can. 
• If you are trying to share the cluster with "legacy" windows code, you can.

Do I have any concerns?

• Somehow I doubt MSFT will be eating their own dogfood here; this may reduce the rate they find problems, leaving it to the end users. Unless they have a fast upgrade rate it may take a while for those changes to roll out. (Look at AWS's update rate: sluggish. Maybe because they've forked)
• To date, Hadoop is optimised for Linux; things like the way it execs() are part of this. There is a risk that changes for Windows performance will conflict with Linux performance. What happens then?
• I forsee a growth in the out-of-depth people trying to use Hadoop and asking newbie questions now related to Windows. Though as we get them already, there may be no change.
• I really wish Windows server had SSH built in rather than telnet. Telnet is dead: move on. We want SSH and SFTP filesystems, and an SFTP filesystem client in both Windows and OS/X. It's the only way to be secure.
• I hope we don't end up in an argument over which underlying OS is best. The answer is: the one you are happy with.

(*) At least for developers. I changed the sprog's password last week as were unhappy with him, and when I passwd'd it back he asked me "why do I use the terminal?". One day he'll learn. I'll know that day as I he'll have changed my password.

[Artwork: unknown, Moon Street, Stokes Croft]

Oracle and Hadoop part 2: Hardware -overkill?

[is the sequel to Part 1]

I've been looking at what Oracle say you should run Hadoop on and thinking "why?"

I don't have the full specs, since all I've seen is slideware on the register implying this is premium hardware, not just x86 boxes with as many HDDs you can fit in a 1U with 1 or 2 multicore CPUs and an imperial truckload of DRAM. In particular, there's mentioning of Infiniband in there.

InfiniBand? Why? Is it to spread the story that the classic location-aware schedulers aren't adequate on "commodity" 12-core 64GB 24TB with 10GbE interconnect? Or are there other plans afoot?

Well, one thing to consider is the lead time for new rack-scale products, some of this exascale stuff will predate the oracle takeover, and the design goals at the time "run databases fast" met Larry's needs more than the rest of the Sun product portfolio -though he still seems to dream of making a $ or two from every mobile phone on the planet.

The question for Oracle has been "how to get from hardware proto to shipping what they can say is the best Oracle server." Well, one tactic is to identify the hardware that runs Oracle better and stop supporting it. It's what they are doing against HP's servers, and will no doubt try against IBM when the opportunity arises. That avoids all debate about which hardware to run Oracle on. It's Oracle's. Next question? Support costs? Wait and see.

While all this hardware development was going on, the massive-low-cost GFS/HDFS filesystem with integrate compute was sneaking up on the sides. Yes, it's easy to say -as Stonebraker did- that MapReduce is a step backwards. But it scales, not just technically, but financially. Look at the spreadsheets here. Just as Larry and Hurd -who also seemed over-fond of the Data Warehouse story- are getting excited about Oracle on Oracle hardware, somewhere your data enters but never leaves(*), somebody has to break them the bad news that people have discovered an alternative way to store and process data. One that doesn't need ultra-high-end single server designs, one that doesn't need oracle licenses, and one that doesn't need you to pay for storage at EMC's current rates. That must have upset Larry, and kept him and the team busy on a response.

What they have done is defensive actions: Hadoop as a way of storing the low value data near Oracle RDBMS, for you to use it as the Extract-Transform-Load part of the story. Where it does fit in, as you can offload some of the grunge work to lower end machines, the storage to SATA. It's no different from keeping log data in HDFS but the high value data in HBase on HDFS, or -better yet IMO- HP Vertica.

For that story to work best, you shouldn't overspec the hardware with things like InfiniBand. So why has that been done?

• Hypothesis 1: margins are better, helps stop people going to vendors (HP, Rackable), that can sell the servers that work best in this new world.
• Hypothesis 2: Oracle's plans in the NoSQL world depend on this interconnect.
• Hypothesis 3: Hadoop MR can benefit from it.

Is Hypothesis 3 valid? Well, in Disk-Locality in Datacenter Computing Considered Irrelevant [Ananthanarayanan2011], Ganesh and colleagues argue that improvements in in-rack and backplane bandwidth will mean you won't care whether your code is running on the same server as your data, or even the same rack as your data. Instead you will worry about whether your data is in RAM or on HDD, as that is what slows you down the most. I agree that even today on 10GbE rack-local is as fast as server-local, but if we could take HDFS out the loop for server-local FS access, that difference may reappear. And while eliminating Ethernet's classic Spanning Tree forwarding algorithm for something like TRILL would be great, it's still going to cost a lot to get a few hundred Terabits/s over the backplane, so in large clusters rack-local may still have an edge. If not, well, there's still the site-local issue that everyone's scared of dealing with today.

Of more immediate interest is Can High-Performance Interconnects Benefit Hadoop Distributed File System?, [Sur2010]. This paper looks at what happens today if you hook up a Hadoop cluster over InfiniBand. They showed it could speed things up, even more if you went to SSD. But go there and -today- you massively cut back on your storage capacity. It is an interesting read, though it irritates me that they fault HDFS for not using the allocateDirect feature of Java NIO, and didn't file a bug or fix for that. See a problem, don't just write a paper saying "our code is faster than theirs". Fix the problem in both codebases and show the speedup is still there -as you've just removed one variable from the mix.


Anyway, even with that paper, 10GbE looks pretty good, it'll be built in to the new servers and if the NIO can be fixed, it's performance may get even closer to InfiniBand. You'd then have to move to alternate RPC mechanisms to get the latency improvements that InfiniBand promises.

Did the Oracle team have these papers in mind when they did the hardware? Unlikely, but they may have felt that IB offers a differentiator over 10GbE. Which it does, in cost terms, limits of scale and complexity of bringing up a rack.

They'd better show some performance benefits for that -either in Hadoop or the NoSQL DB offering they are promising.

(*) People call this the "roach motel" model, but I prefer to refer to Northwick Park Hospital in NW London. Patients enter, but they never come out alive.

A note on distributed computing diagnostics

I've just stuck in my first mildly interesting co-authored Hadoop patch for a while, HADOOP-7466: "Add a standard handler for socket connection problems which improves diagnostics"

It tries to address two problems

Inadequate Diagnostics in the Java Runtime

Despite Java being "the language of the Internet" or whatever Sun used to call it, when you get any kind of networking problem (Connection Refused, No Route to Host, Unknown Host, Socket Timed Out), the standard Java exception messages don't bother to tell you which host isn't responding, what port is refusing connections or anything else useful. In a room with 2000 machines, it's not that useful to know that one of them can't talk to another. You need to know which machine is having problems, what other machine it is trying to talk to, and whether its the HDFS level or something above. But no, the exception text never gets any better, whoever wrote them didn't read Waldo's A Note on Distributed Computing and think that if two machines are near each other nothing can possibly go wrong.

Whatever they were thinking, if they tried to submit exception messages like that to the Hadoop codebase today, the review process would probably bounce them back with a "make this vaguely useful". The patch tries to fix this by taking the exception and the (hostname, port) of the source and destination (if known), and then includes these details in the exception text. This helps people like me know what's gone wrong with our configuration and/or network.

Inadequate understanding of the fundamental network error messages

This is something I despair of. There are people out there that haven't done enough homework to know what a ConnectionRefused exception means, and ask for help when they see it. Again, again and again. Same for all the other common error messages.

The people who are trying to set up Hadoop clusters who don't yet know what these error messages are in way out of their depth. That should be an appendix to Waldo's paper: the many layers of historical code underneath are not transparent; it helps to have read Tanenbaum's "Computer Networking" book, it helps to spend some time writing code at the socket layer, just to understand what goes wrong at that level. Trying to download the Hadoop artifacts and then push it out to a small set of machines without this basic knowledge is dooming these people to days of confusion, which inevitably propagates to the mailing lists and bug trackers. Usually someone posts a stack trace to the -user and -dev lists, then starts repeating it every hour until someone answers; the total cost of wasted time is surprisingly high.

The patch, therefore, also add references to the wiki pages, for ConnectionRefused, UnknownHost, NoRouteToHost, BindException,  SocketTimeout. All of which list some possible causes, and some tips on debugging the problem. And also say : this is your network, your configuration, you are going to have to fix it yourself.

Will it stop people asking for help? Unlikely. But it may get them learn what the messages mean, and why it is a problem on their side. Because it's not my problem.


[Artwork by Sepr]

H work

On my todo list, then is to catch up with what's going on Hadoop, get some of my minor issues checked in and get involved with the fun stuff in Trunk. That includes the 0.23 YARN stuff, but I'm also starting to think there are some data integrity risks that ought be addressed.

First step: building everything. I am particularly excited to see that Hadoop-trunk now requires me to download and build protocol buffers [README]. I shall be updating the relevant wiki pages so that I can remember what to do on the other machines.