Exploring the HDFS Default Value Behaviour

In this post I’ll explore how the HDFS default values really work, which I found to be quite surprising and non-intuitive, so there s a good lesson here.

In my case, I have my local virtual Hadoop cluster, and I have a different client outside the cluster. I’ll just put a file from the external client into the cluster in two configurations.

All the nodes of my Hadoop (1.2.1) cluster have the same hdfs-site.xml file with the same (non-default) value for dfs.block.size (renamed to dfs.blocksize in Hadoop 2.x) of 134217728, which is 128MB. In my external node, I also have the hadoop executables with a minimal hdfs-site.xml.

First, I have set dfs.block.size to 268435456 (256MB) in my client hdfs-site.xml and copied a 400MB file to HDFS:

 ./hadoop fs -copyFromLocal /sw/400MB.file /user/ofir

Checking its block size from the NameNode:

./hadoop fsck /user/ofir/400MB.file -files -blocks -racks
FSCK started by root from / for path /user/ofir/400MB.file at Thu Jan 30 22:21:29 UTC 2014
/user/ofir/400MB.file 419430400 bytes, 2 block(s):  OK
0. blk_-5656069114314652598_27957 len=268435456 repl=3 [/rack03/, /rack03/, /rack02/]
1. blk_3668240125470951962_27957 len=150994944 repl=3 [/rack03/, /rack03/, /rack01/]

 Total size:    419430400 B
 Total dirs:    0
 Total files:    1
 Total blocks (validated):    2 (avg. block size 209715200 B)
 Minimally replicated blocks:    2 (100.0 %)
 Over-replicated blocks:    0 (0.0 %)
 Under-replicated blocks:    0 (0.0 %)
 Mis-replicated blocks:        0 (0.0 %)
 Default replication factor:    1
 Average block replication:    3.0
 Corrupt blocks:        0
 Missing replicas:        0 (0.0 %)
 Number of data-nodes:        6
 Number of racks:        3
FSCK ended at Thu Jan 30 22:21:29 UTC 2014 in 1 milliseconds

So far, looks good – the first block is 256MB.

Now, let’s remove the reference for dfs.block.size from my hdfs-site.xml file on the client. I expected that, since we don’t specify a value in our client, it should not ask for any specific block size, so the actual block size will be the value set in the NameNode and DataNodes – 128MB. However, this is what I got:

./hadoop fs -copyFromLocal /sw/400MB.file /user/ofir/400MB.file2

Checking from the NameNode:

# ./hadoop fsck /user/ofir/400MB.file2 -files -blocks -racks
FSCK started by root from / for path /user/ofir/400MB.file2 at Thu Jan 30 22:27:52 UTC 2014
/user/ofir/400MB.file2 419430400 bytes, 7 block(s):  OK
0. blk_1787803769905799654_27959 len=67108864 repl=3 [/rack03/, /rack03/, /rack01/]
1. blk_3875160333629322317_27959 len=67108864 repl=3 [/rack01/, /rack01/, /rack03/]
2. blk_-3214658865520475261_27959 len=67108864 repl=3 [/rack03/, /rack03/, /rack02/]
3. blk_-3454099016147726286_27959 len=67108864 repl=3 [/rack03/, /rack03/, /rack01/]
4. blk_-4422811025086071415_27959 len=67108864 repl=3 [/rack01/, /rack01/, /rack02/]
5. blk_8134056524051282216_27959 len=67108864 repl=3 [/rack02/, /rack02/, /rack03/]
6. blk_2811491227269477239_27959 len=16777216 repl=3 [/rack02/, /rack02/, /rack03/]

 Total size:    419430400 B
 Total dirs:    0
 Total files:    1
 Total blocks (validated):    7 (avg. block size 59918628 B)
 Minimally replicated blocks:    7 (100.0 %)
 Over-replicated blocks:    0 (0.0 %)
 Under-replicated blocks:    0 (0.0 %)
 Mis-replicated blocks:        0 (0.0 %)
 Default replication factor:    1
 Average block replication:    3.0
 Corrupt blocks:        0
 Missing replicas:        0 (0.0 %)
 Number of data-nodes:        6
 Number of racks:        3
FSCK ended at Thu Jan 30 22:27:52 UTC 2014 in 2 milliseconds

The filesystem under path '/user/ofir/400MB.file2' is HEALTHY

Well, the file definitely has a block size of 64MB, not 128MB! This is actually the default value for dfs.block.size in Hadoop 1.x.

It seems that the value of dfs.block.size is dictated directly by the client, regarding of the cluster setting. If a value is not specified, the client just picks the default value. This finding is not  specific to this parameter – for example, the same thing happens with dfs.replication and others….

So, if you ever wondered why you must have full copy of the Hadoop config files around instead of just pointing to the NameNode and JobTracker, now you know…
Now double check your client setup!

Corrections and comments are welcomed!

P.S. Just as I was publishing it, I found a shorter way to check the block size of a file, that also works from any client:

# ./hadoop fs -stat %o /user/ofir/400MB.file
# ./hadoop fs -stat %o  /user/ofir/400MB.file2

Exploring HDFS Block Placement Policy

In this post I’ll cover how to see where each data block is actually placed. I’m using my fully-distributed Hadoop cluster on my laptop for exploration and the network topology definitions from my previous post – you’ll need a multi-node cluster to reproduce it.


When a file is written to HDFS, it is split up into big chucks called data blocks, whose size is controlled by the parameter dfs.block.size in the config file hdfs-site.xml (in my case – left as the default which is 64MB). Each block is stored on one or more nodes, controlled by the parameter dfs.replication in the same file (in most of this post – set to 3, which is the default). Each copy of a block is called a replica.

Regarding my setup – I have nine nodes spread over three racks. Each rack has one admin node and two worker nodes (running DataNode and TaskTracker), using Apache Hadoop 1.2.1. Please note that in my configuration, there is a simple mapping between host name, IP address and rack number -host name is hadoop[rack number][node number] and IP address is[rack number][node number].

Where should the NameNode place each replica?  Here is the theory:

The default block placement policy is as follows:

  • Place the first replica somewhere – either a random node (if the HDFS client is outside the Hadoop/DataNode cluster) or on the local node (if the HDFS client is running on a node inside the cluster).
  • Place the second replica in a different rack.
  • Place the third replica in the same rack as the second replica
  • If there are more replicas – spread them across the rest of the racks.

That placement algorithm is a decent default compromise – it provides protection against a rack failure while somewhat minimizing inter-rack traffic.

So, let’s put some files on HDFS and see if it behaves like the theory in my setup – try it on yours to validate.

Putting a file into HDFS from outside the Hadoop cluster

As my block size is 64MB,  I created a file (called /sw/big) on my laptop for testing. The file is about 125MB – so it consumes two HDFS blocks. It is not inside one of the data node containers but outside the Hadoop cluster. I then copied it to HDFS twice:

# /sw/hadoop-1.2.1/bin/hadoop fs -copyFromLocal /sw/big /big1
# /sw/hadoop-1.2.1/bin/hadoop fs -copyFromLocal /sw/big /big2

In order to see the actual block placement, of a specific file, I ran hadoop fsck filename -files -blocks -racks from the NameNode server and looked at the top of the report:

# bin/hadoop fsck /big1 -files -blocks -racks
FSCK started by root from / for path /big1 at Mon Jan 06 11:20:01 UTC 2014
/big1 130633102 bytes, 2 block(s):  OK
0. blk_3712902403633386081_1008 len=67108864 repl=3 [/rack03/, /rack03/, /rack02/]
1. blk_381038406874109076_1008 len=63524238 repl=3 [/rack03/, /rack03/, /rack01/]

 Total size:    130633102 B
 Total dirs:    0
 Total files:    1
 Total blocks (validated):    2 (avg. block size 65316551 B)
 Minimally replicated blocks:    2 (100.0 %)
 Over-replicated blocks:    0 (0.0 %)
 Under-replicated blocks:    0 (0.0 %)
 Mis-replicated blocks:        0 (0.0 %)
 Default replication factor:    3
 Average block replication:    3.0
 Corrupt blocks:        0
 Missing replicas:        0 (0.0 %)
 Number of data-nodes:        6
 Number of racks:        3
FSCK ended at Mon Jan 06 11:20:01 UTC 2014 in 3 milliseconds

The filesystem under path '/big1' is HEALTHY

You can see near the end that the NameNode correctly reports it know of six data nodes and three racks. If we look specifically at the block report:

0. blk_37129... len=67108864 repl=3 [/rack03/, /rack03/, /rack02/]
1. blk_38103... len=63524238 repl=3 [/rack03/, /rack03/, /rack01/]

We can see:

  • Block 0 of file big1 is on hadoop23 (rack 2), hadoop32 (rack 3), hadoop33 (rack 3)
  • Block 1 of file big1 is on hadoop13 (rack1), hadoop32 (rack 3), hadoop33 (rack 3)

Running the same report on big2:

# bin/hadoop fsck /big2 -files -blocks -racks
FSCK started by root from / for path /big2 at Mon Jan 06 11:49:36 UTC 2014
/big2 130633102 bytes, 2 block(s):  OK
0. blk_-2514176538290345389_1009 len=67108864 repl=3 [/rack02/, /rack02/, /rack03/]
1. blk_316929026766197453_1009 len=63524238 repl=3 [/rack01/, /rack01/, /rack03/]

We can see that:

  • Block 0 of file big2 is on hadoop33 (rack 3), hadoop22 (rack 2), hadoop23 (rack 2)
  • Block 1 of file big2 is on hadoop33 (rack 3), hadoop12 (rack 1), hadoop13 (rack 1)

Conclusion – we can see that when the HDFS client is outside the Hadoop cluster, for each block, there is a single replica on a random rack and two other replicas on a different, random rack, as expected.

Putting a file into HDFS from within the Hadoop cluster

OK, let’s repeat the exercise, now copying the big file from a local path on one of the data nodes (I used hadoop22) to HDFS:

# /usr/local/hadoop-1.2.1/bin/hadoop fs -copyFromLocal big /big3
# /usr/local/hadoop-1.2.1/bin/hadoop fs -copyFromLocal big /big4

We expect to see the first replica of all blocks to be local – on node hadoop22.
Running the report on big3:

# bin/hadoop fsck /big3 -files -blocks -racks
FSCK started by root from / for path /big3 at Mon Jan 06 12:10:03 UTC 2014
/big3 130633102 bytes, 2 block(s):  OK
0. blk_-509446789413926742_1013 len=67108864 repl=3 [/rack03/, /rack03/, /rack02/]
1. blk_1461305132201468085_1013 len=63524238 repl=3 [/rack03/, /rack03/, /rack02/]

We can see that:

  • Block 0 of file big3 is on hadoop22 (rack 2), hadoop33 (rack 3), hadoop32 (rack 3)
  • Block 1 of file big3 is on hadoop22 (rack 2), hadoop33 (rack 3), hadoop32 (rack 3)

And for big4:

# bin/hadoop fsck /big4 -files -blocks -racks
FSCK started by root from / for path /big4 at Mon Jan 06 12:12:52 UTC 2014
/big4 130633102 bytes, 2 block(s):  OK
0. blk_-8460635609179877275_1014 len=67108864 repl=3 [/rack03/, /rack03/, /rack02/]
1. blk_8256749577534695387_1014 len=63524238 repl=3 [/rack01/, /rack02/, /rack01/]
  • Block 0 of file big4 is on hadoop22 (rack 2), hadoop32 (rack 3), hadoop33 (rack 3)
  • Block 1 of file big4 is on hadoop22 (rack 2), hadoop12 (rack 1), hadoop13 (rack 1)

Conclusion – we can see that when the HDFS client is inside the Hadoop cluster for each block, there is a single replica on the local node (hadoop22 in this case) and two other replicas on a different,random rack, as expected.

Just for fun – create a file with more than three replicas

We can override Hadoop parameters when invoking the shell using -D option. Let’s use it to create a file with four replicas from node hadoop22:

# /usr/local/hadoop-1.2.1/bin/hadoop fs -D dfs.replication=4 -copyFromLocal ~/big /big5

Running the report on big5:

# bin/hadoop fsck /big5 -files -blocks -racks
FSCK started by root from / for path /big5 at Mon Jan 06 12:36:28 UTC 2014
/big5 130633102 bytes, 2 block(s):  OK
0. blk_-4060957873241541489_1015 len=67108864 repl=4 [/rack01/, /rack03/, /rack03/, /rack02/]
1. blk_8361194277567439305_1015 len=63524238 repl=4 [/rack03/, /rack01/, /rack01/, /rack02/]

We can see that:

  • Block 0 of file big3 is on hadoop22 (rack 2), hadoop33 (rack 3), hadoop32 (rack3), hadooop12 (rack1)
  • Block 1 of file big3 is on hadoop22 (rack 2), hadoop13 (rack 1), hadoop12 (rack1), hadooop33 (rack3)

So far, works as expected! That’s a nice surprise…

Finally, let’s try to break something 🙂

What will happen when we’ll try to specify more replicas than data nodes?

While it is “stupid”, it will likely happen in pseudo-distributed config every time you try dfs.replication > 1, as you have only one DataNode.

# /usr/local/hadoop-1.2.1/bin/hadoop fs -D dfs.replication=7 -copyFromLocal ~/big /big7

We do have only six data nodes. Running the report on big7:

# bin/hadoop fsck /big7 -files -blocks -racks
FSCK started by root from / for path /big7 at Mon Jan 06 12:40:05 UTC 2014
/big7 130633102 bytes, 2 block(s):  Under replicated blk_1565930173693194428_1016. Target Replicas is 7 but found 6 replica(s).
 Under replicated blk_-2825553771191528109_1016. Target Replicas is 7 but found 6 replica(s).
0. blk_1565930173693194428_1016 len=67108864 repl=6 [/rack01/, /rack01/, /rack03/, /rack03/, /rack02/, /rack02/]
1. blk_-2825553771191528109_1016 len=63524238 repl=6 [/rack01/, /rack01/, /rack03/, /rack03/, /rack02/, /rack02/]

 Total size:    130633102 B
 Total dirs:    0
 Total files:    1
 Total blocks (validated):    2 (avg. block size 65316551 B)
 Minimally replicated blocks:    2 (100.0 %)
 Over-replicated blocks:    0 (0.0 %)
 Under-replicated blocks:    2 (100.0 %)
 Mis-replicated blocks:        0 (0.0 %)
 Default replication factor:    3
 Average block replication:    6.0
 Corrupt blocks:        0
 Missing replicas:        2 (16.666666 %)
 Number of data-nodes:        6
 Number of racks:        3
FSCK ended at Mon Jan 06 12:40:05 UTC 2014 in 1 milliseconds

We can see that each data node has a single copy of each block, and fsck notifies us that the two blocks are under-replicated. Nice!

Changing the replication factor of an existing file.

Since this warning in fsck might be annoying (and depending on your monitoring scripts, sets various alarms…), we should change the replication factor of this file:

# bin/hadoop fs -setrep 6 /big7
Replication 6 set: hdfs://hadoop11:54310/big7

In this case, the NameNode won’t immediate delete the extra replicas:

# bin/hadoop fsck /big7 -files -blocks -racks
FSCK started by root from / for path /big7 at Mon Jan 06 12:51:00 UTC 2014
/big7 130633102 bytes, 2 block(s):  OK
0. blk_1565930173693194428_1016 len=67108864 repl=6 [/rack01/, /rack01/, /rack03/, /rack03/, /rack02/, /rack02/]
1. blk_-2825553771191528109_1016 len=63524238 repl=6 [/rack01/, /rack01/, /rack03/, /rack03/, /rack02/, /rack02/]

 Total size:    130633102 B
 Total dirs:    0
 Total files:    1
 Total blocks (validated):    2 (avg. block size 65316551 B)
 Minimally replicated blocks:    2 (100.0 %)
 Over-replicated blocks:    2 (100.0 %)
 Under-replicated blocks:    0 (0.0 %)
 Mis-replicated blocks:        0 (0.0 %)
 Default replication factor:    3
 Average block replication:    6.0
 Corrupt blocks:        0
 Missing replicas:        0 (0.0 %)
 Number of data-nodes:        6
 Number of racks:        3
FSCK ended at Mon Jan 06 12:51:00 UTC 2014 in 1 milliseconds

The filesystem under path '/big7' is HEALTHY

If you are in a hurry, adding -w to the command should trigger immediate replication level change:

# bin/hadoop fs -setrep 5 -w /big7
Replication 5 set: hdfs://hadoop11:54310/big7
Waiting for hdfs://hadoop11:54310/big7 .... done

Exploring The Hadoop Network Topology

In this post I’ll cover how to define and debug a simple Hadoop network topology.


Hadoop is designed to run on large clusters of commodity servers – in many cases spanning many physical racks of servers. A physical rack is in many cases a single point of failure (for example, having typically a single switch for lower cost), so HDFS tries to place block replicas on more than one rack. Also, there is typically more bandwidth within a rack than between the racks, so the software on the cluser (HDFS and MapReduce / YARN) can take it into account. This leads to a question:

How does the NameNode know the network topology?

By default, the NameNode has no idea which node is in which rack. It therefore by default assumes that all nodes are in the same rack, which is likely true for small clusters. It calls this rack “/default-rack“.

So, we have to teach Hadoop our cluster network topology – the way the nodes are grouped into racks. Hadoop supports a pluggable rack topology implementation – controlled by the parameter topology.node.switch.mapping.impl in core-site.xml, which specifies a java class implementation. The default implementation is using a user-provided script, specified in topology.script.file.name in the same config file, a script that gets a list of IP addresses or host names and returns a list of rack names (in Hadoop2: net.topology.script.file.name). The script will get up to topology.script.number.args parameters per invocation, by default up to 100 requests per invocation (in Hadoop2: net.topology.script.number.args).

In my case, I set it to /usr/local/hadoop-1.2.1/conf/topology.script.sh which I copied from the Hadoop wiki. I just made a few changes – I changed the path to my conf directory in the second line, added some logging of the call in the third line and changed the default rack name near the end to /rack01:

echo `date` input: $@ >> $HADOOP_CONF/topology.log
while [ $# -gt 0 ] ; do
  exec< ${HADOOP_CONF}/topology.data
  while read line ; do
    ar=( $line )
    if [ "${ar[0]}" = "$nodeArg" ] ; then
  if [ -z "$result" ] ; then
#    echo -n "/default/rack "
     echo -n "/rack01"
    echo -n "$result "

The script basically just parses a text file (topology.data) that holds a mapping from IP address or host name to rack name. Here are the content of my file:

hadoop11        /rack01
hadoop12        /rack01
hadoop13        /rack01
hadoop14        /rack01
haddop15        /rack01
hadoop21        /rack02
hadoop22        /rack02
hadoop23        /rack02
hadoop24        /rack02
hadoop25        /rack02
hadoop31        /rack03
hadoop32        /rack03
hadoop33        /rack03
hadoop34        /rack03
hadoop35        /rack03      /rack01      /rack01      /rack01      /rack01      /rack01      /rack02      /rack02      /rack02      /rack02      /rack02      /rack03      /rack03      /rack03      /rack03      /rack03

A bit long, but pretty straight forward. Please note that in my configuration, there is a simple mapping between host name, IP address and rack number – the host name is hadoop[rack number][node number] and IP address is[rack number][node number].

You could of course write any logic into the script. For example, using my naming convention, I could have written a simple script that just takes the second-to-last character and translate it to rack number – that would work on both IP addresses and host names. As another example – I know some companies allocate IP addresses for their Hadoop cluster as x.y.[rack_number].[node_number] – so they can again just parse it directly.

Before we test the script, if you have read my previous post on LXC setup, please note that I made  a minor change – I switched to a three-rack cluster, to make the block placement more interesting.  So, my LXC nodes are:

hadoop11 namenode zookeeper
hadoop12 datanode tasktracker
hadoop13 datanode tasktracker
hadoop21 jobtracker zookeeper
hadoop22 datanode tasktracker
hadoop23 datanode tasktracker
hadoop31 secondarynamenode hiveserver2 zookeeper
hadoop32 datanode tasktracker
hadoop33 datanode tasktracker

OK, now that we covered the script, let’s start the NameNode and see see what gets logged:

# bin/hadoop-daemon.sh --config conf/ start namenode
starting namenode, logging to /usr/local/hadoop-1.2.1/libexec/../logs/hadoop-root-namenode-hadoop11.out
# cat conf/topology.log 
Mon Jan 6 19:04:03 UTC 2014 input:

As the NameNode started, it asked in a single called what is the rack name of all our nodes. This is what the script returns to the NameNode:

# conf/topology.script.sh
/rack02 /rack02 /rack01 /rack01 /rack03 /rack03

Why did the NameNode send all the IP addresses of the DataNodes in a single call? In this case, I have pre-configured another HDFS parameter called dfs.hosts in hdfs-site.xml. This parameter points to a file with a list of all nodes that are allowed to run a data node. So, when the NameNode started, it just asked for the mapping of all known data node servers.

What happens if you don’t use dfs.hosts? To check, I removed this parameter from my hdfs-site.xml file, restarted the NameNode and started all the data nodes. In this case, the NameNode called the topology script once per DataNode (when they first reported their status to the NameNode):

# cat topology.log 
Mon Jan 6 19:04:03 UTC 2014 input:
Mon Jan 6 19:07:53 UTC 2014 input:
Mon Jan 6 19:07:53 UTC 2014 input:
Mon Jan 6 19:07:53 UTC 2014 input:
Mon Jan 6 19:07:53 UTC 2014 input:
Mon Jan 6 19:07:54 UTC 2014 input:
Mon Jan 6 19:07:54 UTC 2014 input:

I hope this post has enough data for you to hack and QA your own basic network topologies. In the next post I hope to investigate block placement – how to see where HDFS is actually putting each copy of each block under various conditions.

Creating a virtualized fully-distributed Hadoop cluster using Linux Containers

TL;DR Why and how I created a working 9-node Hadoop Cluster on my  laptop

In this post I’ll cover why I wanted to have a decent multi-node Hadoop cluster on my laptop, why I chose not to use virtualbox/VMware player, what is LXC (Linux Containers) and how did I set it up. The last part is a bit specific to my desktop O/S (Ubuntu 13.10).

Why install a fully-distributed Hadoop cluster on my laptop?

Hadoop has a “laptop mode” called pseudo-distributed mode. In that mode, you run a single copy of each service (for example, a single HDFS namenode and a single HDFS datanode), all listening under localhost.

This feature is rather useful for basic development and testing – if all you want is write some code that uses the services and check if it runs correctly (or at all). However, if your focus is the platform itself – an admin perspective, than that mode is quite limited. Trying out many of Hadoop’s feature requires a real cluster – for example replication, failure testing, rack awareness, HA (of every service types) etc.

Why VirtualBox or VMware Player are not a good fit?

When you want to run several / many virtual machines, the overhead of these desktop virtualization sotware is big. Specifically, as they run a full-blown guest O/S inside them, they:

  1. Consume plenty of RAM per guest – always a premium on laptops.
  2. Consume plenty of disk space – which is limited, especially on SSD.
  3. Take quite a while to start / stop

To run many guests on a laptop, a more lightweight infrastructure is needed.

What is LXC (Linux Containers)

LXC is a lightweight virtualization solution for Linux, sometimes called “chroot on steroids”. It leverages a set of Linux kernel features (control groups, kernel namespaces etc) to provide isolation between groups of processes running on the host. In other words, in most cases the containers (virtual machines) are using the host kernel and operating system, but are isolated as if they are running on their own environent (including their own IP addresses etc).

In practical terms, I found that the LXC containers start in a sub-second. They have no visible memory overhead  – for example, if a container runs the namenode, only the namenode consumes RAM, based on its java settings. They also consume very little disk space, and their file system is just stored directly in the host file system (under /var/lib/lxc/container_name/rootfs), which is very useful. You don’t need to statically decide how much RAM or virtual cores to allocate each (though you could) – they consume just what they need. Actually, while the guests are isolated from each other, you can see all their processes together in the host using the ps. command as root..

Some other notes – LXC is already used by Apache Mesos and Dockers, amongst others. A “1.0” release is targeted next month (February). If you are using RHEL/CentOS, LXC is provided as a “technical preview” on RHEL 6 (likely best to use a current update).

If you want a detailed introduction, Stéphane Graber which is one of the LXC maintainer started an excellent ten-post series on LXC a couple of weeks ago.

So, how to set it up?

The following are some of my notes. I only tried it on my laptop, with Ubuntu 13.10 64-bit as a guest, so if you have a different O/S flavor, expect some changes.

All commands below are as root.
start by installing LXC:

apt-get install lxc

The installation also creates a default virtual network bridge device for all containers (lxcbr0). Next, create your first Ubuntu container called test1 – it will take a few minutes as some O/S packages will be downloaded (but they will be cached, so the next containers will be created in a few seconds):

lxc-create -t ubuntu -n test1

Start the container with:

lxc-start -d -n test1

List all your containers (and their IP addreses) with:

lxc-ls --fancy

and open a shell on it with:

lxc-attach -n test1

stop the container running on your host:

lxc-stop -n test1

You can also clone a container (lxc-clone) or freeze/unfreeze a running one (lxc-freeze / lxc-unfreeze). LXC also has a web GUI with some nice visualizations (though some of it was broken for me), install it with:

wget http://lxc-webpanel.github.io/tools/install.sh -O - | bash

and access it with http://localhost:5000 (default user/password is admin/admin):


Now, some specific details for an Hadoop container

1. Install a JVM inside the container. The default LXC Ubuntu container has a minimal set of packages . At first, I installed openjdk in it:

apt-get install openjdk-7-jdk

However, I decided to switched to Oracle JDK as that is what’s typically being used. That required some extra steps (the first step adds the “add-apt-repository” command, the others set a java7 repository to ease installation and updates):

apt-get install software-properties-common
add-apt-repository ppa:webupd8team/java
apt-get update
apt-get install oracle-java7-installer

2. Setting the network

By default, LXC uses DHCP to provide dynamic IP addresses to containers. As Hadoop is very sensitive to IP addresses (it occasionally uses them instead of host names), I decided that I want static IPs for my Hadoop containers.

In Ubuntu, the lxc bridge config file is /etc/default/lxc-net – I uncommented the following line:


and created /etc/lxc/dnsmasq.conf with the following contents:


That should cover more containers than I’ll ever need… My naming convention is that the host name is hadoop[rack number][node number] and the IP is[rack number][hostname]. For example, host hadoop13 is located at rack 1 node 3 with IP address of 10.0.113. This way it is easy to figure out what’s going on even if the logs only have IP.

I’ve also added all of the potential containers to my /etc/hosts and copied them to hadoop11, which I use as a template to clone the rest of the nodes:	hadoop11      hadoop12      hadoop13      hadoop14      hadoop15      hadoop16      hadoop17      hadoop18      hadoop19      hadoop21      hadoop22      hadoop23      hadoop24      hadoop25      hadoop26      hadoop27      hadoop28      hadoop29

I ran into one  piece of ugliness regarding DHCP. I use hadoop11 container as a template, so I as I develop my environment I have destroyed the rest of the containers and cloned them again a few times. At that point, they got a new dynamic IP – it seems the DHCP server remembered that the static IP that I wanted was already allocated. Eventually, I added this to the end of  my “destroy all nodes” script to solve that:

service lxc-net stop
rm /var/lib/misc/dnsmasq.lxcbr0.leases 
service lxc-net start

If you run into networking issues – I recommend checking the Ubuntu-specific docs, as lots of what you find on the internet is scary network hacks that are likely not needed in Ubuntu 13.10 (and didn’t work for me).

Another setup thing – if you run on BTRFS (a relatively new copy-on-write filesystem), the lxc-clone should detect it and leverage it. It doesn’t seem to work for me, but instead copying files directly into /var/lib/lxc/container_name/rootfs using cp –reflink works as expected (you can verify disk space after copying by waiting a few seconds and then issuing btrfs file df /var/lib/lxc/ )

3. Setting Hadoop

This is pretty standard… You should just set up ssh keys, maybe create some O/S users for Hadoop, download Hadoop bits from Apache or a vendor and get going… Personally, I used Apache Hadoop (1.2.1 and 2.2.0) – I have set things up on a single node and then clone it to create a cluster.

In addition, to ease my tests, I’ve been scripting the startup, shutdown and full reset of the environment based on a config file that I created. For example, here is my current config file for Apache Hadoop 1.2.1 (the scripting works nicely but is still evolving – ping me if you are curious):

hadoop11 namenode zookeeper
hadoop12 datanode tasktracker
hadoop13 datanode tasktracker
hadoop14 datanode tasktracker
hadoop21 jobtracker zookeeper
hadoop22 datanode tasktracker
hadoop23 datanode tasktracker
hadoop24 datanode tasktracker
hadoop29 secondarynamenode hiveserver2 zookeeper
Obviously, you can set a smaller cluster, based on your cores / RAM / disk / workload etc.
If you’re working with Hadoop, having a full blown cluster on your laptop is just awesome… Stay tuned for some examples in the coming weeks 🙂

The end of the monolithic database engine dream

It seems I like calling my posts “The End of…” 🙂

Anyway, Rob Klopp wrote an interesting post titled “Specialized Databases vs. Swiss Army Knives“. In it, he argues with Stonebraker’s claim that the database market will split to three-to-six categories of databases, each with its own players. Rob counter claims by saying that data is typically used in several ways, so it is cumbersome to have several specialized databases instead of one decent one (like Hana of course…).

I have a somewhat different perspective. In the past, let’s say ten years ago, I was sure that Oracle  database is the right thing to throw at any database challenge, and I think many in the industry shared that feeling (each with his/her favorite database, of course). There was a belief that a single database engine could be smart enough, flexible enough, powerful enough to handle almost everything.

That belief is now history. As I will show, it is now well understood and acknowledged by all the major vendors that a general-purpose database engine just can’t compete in all high-end niches. HOWEVER, the existing vendors are, as always, adapting. All of them are extending their databases to offer multiple database engines inside their product, each for a different use case.

The leader here seems to be Microsoft SQL Server. SQL Server 2014 (cuirrently at CTP2) comes with three separate database engines. In addition to the existing engine, they introduced Hekaton – an in-memory OLTP engine that looks very promising. They also delivered a brand new implementation of their columnar format – now called clustered columnstore index – which is now fully updatable and is actually not an index – it is a primary table storage format with all the usual plumbing (delta trees with a tuple mover process when enough rows have accumulated).

Oracle has been promoting two engines for a long while – TimesTen for low-latency, intensive OLTP (in-memory of course) ,and the Oracle database. Within the Oracle database, they are planning to introduce a second engine for data warehousing in the next release (with an in-memory columnar format) – which I discussed in the past. And IBM DB2 have already introduced an in-memory columnar engine called BLU Acceleration six months ago.

Now, why do I call them engines? It seems to me all these are major, intrusive features, with whole new on-disk formats, memory representations, processing logic and code paths, well beyond a new index type or similar change. In addition, some come with different or no locking/latching and other dramatic internal changes.

So, to wrap it up, while I believe that specialized engines is a new must, the existing players are working to implement multiple engines within their products to keep them relevant. As for having a single decent database for everything – if it does support the required high performance and scalability, with low cost and rich enough functionality (HA, security, resource management, great optimizer etc)… There is always place for a single database for different workloads, if and when it delivers.

Industry Standard SQL-on-Hadoop benchmarking?

Earlier today a witty comment I made on twitter led to a long and heated discussion about vendor exaggerations regarding SQL-on-Hadoop relative performance. It started as a link to this post by Hyeong-jun Kim, CTO and Chief Architect at Gruter. That post discusses some ways that vendor exaggerate and suggests to verify these claims with your own data and queries.

Anyway, Milind Bhandarkar from Pivotal suggested that joining an industry standard benchmarking effort might be the right think. I disagree, wanted to elaborate on that:

Industry Standard SQL-on-Hadoop benchmark won’t improve a thing

  • These benchmarks (at least in the SQL space) don’t help users to pick a technology. I’ve never heard of a customer who picked some solution because it lead the performance or price/performance list of TPC-C or TPC-H.
    Customers will always benchmark on their data and their workload…
  • …And they do so because in this space, many of the small variations in data (ex: data distribution within a column) or workload (SQL features, concurrency, transaction type mix) will have dramatic impact on the results.
  • So, the vendors who write the benchmark will fight to death to have the benchmark highlight their (exisitng) strengths. If the draft benchmark will show them at the bottom, they’ll retire and bad mouth the benchmark for not incorporating their suggestions.
  • Of course, the close-source players still won’t allow publishing benchmark results – the “DeWitt Clause” (right Milind? What about HAWK?)
  • And even with a standard, all vendors will still use micro-benchmarks to highlight the full extent of new features and optimizations, so the rebuttals and flame wars will not magically end.

What I think is the right thing for users

Since vendors (and users) will not agree on a single benchmark, the next best thing is that each player will develop their own benchmark(s), but will make it easily reproducible.

For example, share the dataset, the SQLs, the specific pre-processing if any, the specific non-default config options if any, the exact SW versions involved, the exact HW config and hopefully – provide the script that runs the whole benchmark.

This would allow the community and ecosystem – users, prospects and vendors – to:

  • Quickly reproduce the results (maybe on a somewhat different config).
  • Play with different variations to learn how stable are the conclusions (different file formats, data set size, SQL and parameter variations any many more).
  • Share their result in the open, using the same disclosure, for everyone to learn from it or respond to it.

Short version – community over committee.

Why that also won’t happen

Sharing a detailed, easily reproducible report is great for smart users who want to educate themselves and choose the right product.

However, there is nearly zero incentive for vendors and projects to share it, especially for the leaders. Why? Because they are terrified that a competitor will use it to show that he is way faster… That could be the ultimate marketing fail (a small niche player afford it, since they have little to lose).

Some other reasonable excuses – there could be a dependency on internal testing frameworks, scripts or non-public datasets, not enough resources to clean up and document each micro-benchmark or to follow up with everyone etc.

Also, such disclosure may prevent marketing / management from highlighting some results out of context (or add wishful thinking)… Not sure many are willing to report to their board – “sales are down since we told everyone that our product is not yet good enough”.
For example – I haven’t yet seen a player claiming “Great Performance! We are now 0.7x faster than the current performance leader!”. Somehow you’ll need to claim that you are great, even if for some a specific scenario under specific (undisclosed?) constraints.


  • You can’t really optimize picking the right tool by building a universal performance benchmark (and picking is not only about performance).
  • Human interests (commercial and others) will generate friction as all compete for some mindshare. Social pressure might sometimes help to make competitors support civilized technical discussion, but only rarely.
  • when sharing performance numbers, please be nice – share what you can and don’t mislead.
  • As a user, try to learn and verify before making big commitments, and be prepare to screw up occasionally…

Big Data PaaS part 1 – Amazon Redshift

Last month I attended AWS Summit in Tel-Aviv. It was a great event, one of the largest local tech events I’ve seen. Anyway, a session on Redshift got me thinking.

When Amazon Redshift was announced (Nov 2012), I was working at Greenplum. I remember that while Redshift pricing was impressive, I mostly dismissed it at that time due to some perceived gaps in functionality and being in a “limited beta” status. Back to last month’s session, I came to see what’s new and was taken by surprise by the tone of the presentation.

As a database expert, I thought I would hear about the implementation details – the sharding mechanism, the join strategies, the various types of columnar encoding and compression, the pipelined data flow, the failover design etc… While a few of these were briefly mentioned, they were definitely not the focus. Instead, the main focus was the value of getting your data warehouse in a PaaS platform.

So what is the value? It is a combination of several things. Lower cost is of course a big one – it allows smaller organization to have a decent data warehouse. But it is much more than cost – being a PaaS means you can immediately start working without worrying about everything from a complex, time-consuming POC to all the infrastructure work for successful production-grade deployment. Just provision a production cluster for a day or two, try it out on your data and queries, and if you like the performance (and cost) – simply continue to use it. It allows a very safe play – or as they put it “low cost of failure“. In addition, a PaaS environment can handle all the ugly infrastructure tasks around MPP databases or appliances that typically block adoption – backups, DR, connectivity etc.
Let me elaborate on these points:

I guess anyone who did a DW POC can relate to the following painful process. You have to book the POC many weeks in advance, pick from several uneasy options (remote POC, fly to vendor central site or coordinate a delivery to your data center), pick in advance a small subset of data and queries to test, don’t use your native BI/ETL tools, rely on the vendor experts to run and tune the POC, finish with a lot of open questions as you ran out of time, pick something, pay a lot of money, wait a couple of months for delivery, start migration / testing / re-tuning (or even DW design and implementation) and several months later you will likely realize how good (or bad) your choice really was… This is always a very high risk maneuver, and the ability to provision a production environment, try it out and if you like it, just keep it without a huge upfront commitment is a very refreshing concept.

Same goes for many “annoying” infrastructure bits. For example – backup, recovery and DR. Typically these will not be tested in a real-world configuration as part of a POC due to various constraints, and the real tradeoff between feasible options in cost, RPO and RTO will not seriously evaluated or in many cases not even considered. Having a working backup included with Redshift – meaning, the backup functionality with underlying infrastructure and storage – is again huge. Another similar one is patching – it is nice that it’s Amazon’s problem.

Last of these is scaling. With an appliance, scaling out is painful. You order an (expensive) upgrade, wait a couple of month for it to be install, then try to roll it out (which likely involves many hours of storage re-balancing), then pray it works. Obviously you can’t scale down to reduce costs. With Redshift, they provision a second cluster (while your production is switched to read-only mode), and switch you over to the new one “likely within an hour”. Of course, with Redshift you can scale up or down on demand, or even turn off the cluster when not needed (but there are some pricing gotchas as there is a strong pricing bias to a 3-year reserved instances).

If you’re interested, you can find Guy Ernest’s presentation here .During his presentation, most slides were hidden as he and Intel had only 45 minutes – I see now that the full slide deck actually does have some slides full of implementation details 🙂

BTW –  another thing that I was curious about was – can the Amazon team significantly evolve the Redshift code base given its external origin(ParAccel)? I assumed that it is not a trivial task. Well, I just read last week that AWS are releasing a big functionality upgrade or Redshift (plus some more bits), so I think that one is also off the table.

It would be interesting to see if Redshift would now gain more traction, especially as it got more integrated into the AWS offering and workflow.