DDevelopment & Design

How to run multiple guest OS in QEMU?

This weekend I’ve been fiddling with

QEMU. I’ve installed OpenBSD on a single image and wanted to have two instances of it communicating via network. Installing the system was easy, but the networking setup was quite a pain. See how I did that… To make QEMU instances communicate with each other I needed to plug them to a “network”. That’s why I’ve created a bridge to which Virtual Instances would connect to.

I’ve used the following script: 

#!/bin/bash                                                                                                                                                 
# 1st, release all DHCP address and remove all IP address associated
# with the original eth0
#/sbin/dhcpcd -k
kill pidof dhclient
/sbin/ip addr flush eth0
# then take the interface down so we can rename it
/sbin/ip link set eth0 down
# now rename the original eth0 to reth0 (Real ETH0)
nameif reth0 00:24:81:43:61:5b
# OK, bring the same interface (with new name though) back up
/sbin/ip link set reth0 up
# 2nd let's create a bridge called eth0 so other programs think they are
# talking to the same old interface (actually they will talk to the
# bridge which is a clone of the original eth0 - with name MAC addr)
/usr/sbin/brctl addbr eth0
# then add both origianl eth0 and tap1 device to the bridge
/sbin/brctl addif eth0 tap1
/usr/sbin/brctl addif eth0 reth0
echo "showing bridge mac addresses"
/usr/sbin/brctl showmacs eth0
# 3rd, we need to bring the newly created bridge UP
/sbin/ip link set eth0 up
# 4th, renew the DHCP address if possible
#/sbin/dhcpcd -n
dhclient eth0
/sbin/ip addr show

Then I just needed to start Qemu with this command line:

sudo qemu openbsd-4.7.img  -net tap -net nic,macaddr=52:54:00:12:34:57,model=ne2k_pci

Since I’ve set up bridge for Qemu instances, I’ve plugged TAP interfaces into it. That’s why I’ve needed to specify this in my qemu exec line. I’ve also added macaddress setting since both my instances were getting the same one. And that’s all! It works like a charm. Now on to some harder things!

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DDevelopment & Design

Recently at storm-users

I've been reading through storm-users Google Group recently. This resolution was heavily inspired by Adam Kawa's post "Football zero, Apache Pig hero". Since I've encountered a lot of insightful and very interesting information I've decided to describe some of those in this post.

  • nimbus will work in HA mode - There's a pull request open for it already... but some recent work (distributing topology files via Bittorrent) will greatly simplify the implementation. Once the Bittorrent work is done we'll look at reworking the HA pull request. (storm’s pull request)

  • pig on storm - Pig on Trident would be a cool and welcome project. Join and groupBy have very clear semantics there, as those concepts exist directly in Trident. The extensions needed to Pig are the concept of incremental, persistent state across batches (mirroring those concepts in Trident). You can read a complete proposal.

  • implementing topologies in pure python with petrel looks like this:

class Bolt(storm.BasicBolt):
    def initialize(self, conf, context):
       ''' This method executed only once '''
        storm.log('initializing bolt')

    def process(self, tup):
       ''' This method executed every time a new tuple arrived '''       
       msg = tup.values[0]
       storm.log('Got tuple %s' %msg)

if __name__ == "__main__":
    Bolt().run()

  • Fliptop is happy with storm - see their presentation here

  • topology metrics in 0.9.0: The new metrics feature allows you to collect arbitrarily custom metrics over fixed windows. Those metrics are exported to a metrics stream that you can consume by implementing IMetricsConsumer and configure with Config.java#L473. Use TopologyContext#registerMetric to register new metrics.

  • storm vs flume - some users' point of view: I use Storm and Flume and find that they are better at different things - it really depends on your use case as to which one is better suited. First and foremost, they were originally designed to do different things: Flume is a reliable service for collecting, aggregating, and moving large amounts of data from source to destination (e.g. log data from many web servers to HDFS). Storm is more for real-time computation (e.g. streaming analytics) where you analyse data in flight and don't necessarily land it anywhere. Having said that, Storm is also fault-tolerant and can write to external data stores (e.g. HBase) and you can do real-time computation in Flume (using interceptors)

That's all for this day - however, I'll keep on reading through storm-users, so watch this space for more info on storm development.

I've been reading through storm-users Google Group recently. This resolution was heavily inspired by Adam Kawa's post "Football zero, Apache Pig hero". Since I've encountered a lot of insightful and very interesting information I've decided to describe some of those in this post.

  • nimbus will work in HA mode - There's a pull request open for it already... but some recent work (distributing topology files via Bittorrent) will greatly simplify the implementation. Once the Bittorrent work is done we'll look at reworking the HA pull request. (storm’s pull request)

  • pig on storm - Pig on Trident would be a cool and welcome project. Join and groupBy have very clear semantics there, as those concepts exist directly in Trident. The extensions needed to Pig are the concept of incremental, persistent state across batches (mirroring those concepts in Trident). You can read a complete proposal.

  • implementing topologies in pure python with petrel looks like this:

class Bolt(storm.BasicBolt):
    def initialize(self, conf, context):
       ''' This method executed only once '''
        storm.log('initializing bolt')

    def process(self, tup):
       ''' This method executed every time a new tuple arrived '''       
       msg = tup.values[0]
       storm.log('Got tuple %s' %msg)

if __name__ == "__main__":
    Bolt().run()

  • Fliptop is happy with storm - see their presentation here

  • topology metrics in 0.9.0: The new metrics feature allows you to collect arbitrarily custom metrics over fixed windows. Those metrics are exported to a metrics stream that you can consume by implementing IMetricsConsumer and configure with Config.java#L473. Use TopologyContext#registerMetric to register new metrics.

  • storm vs flume - some users' point of view: I use Storm and Flume and find that they are better at different things - it really depends on your use case as to which one is better suited. First and foremost, they were originally designed to do different things: Flume is a reliable service for collecting, aggregating, and moving large amounts of data from source to destination (e.g. log data from many web servers to HDFS). Storm is more for real-time computation (e.g. streaming analytics) where you analyse data in flight and don't necessarily land it anywhere. Having said that, Storm is also fault-tolerant and can write to external data stores (e.g. HBase) and you can do real-time computation in Flume (using interceptors)

That's all for this day - however, I'll keep on reading through storm-users, so watch this space for more info on storm development.