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From Bert Corderman <bertc...@gmail.com>
Subject Re: Cluster design distribution and JBOD vs RAID
Date Wed, 23 Apr 2014 14:45:58 GMT
thanks again for your response and sorry for the delay, but I was out for a
few days.  I jsut wrapped up a bunch of performance testing, I will be
sending link out to the user group here shortly.

Bert


On Thu, Apr 17, 2014 at 8:00 PM, Bello, Bob <Bob.Bello@dish.com> wrote:

> Some feedback from your feedback.
>
> BERT> We have several uses cases we are looking at kafka for.  Today we are
> just using the file system to buffer data between our systems.  We are
> looking at uses cases that have varying message sizes of 200, 300, 1000,
> 2200 bytes
>
> BOB> Since you are using small message size, watch out or large index
> files. You can stuff a lot of messages in to the default log file size of
> 512MB. We use 1GB log files before rolling them.
>
>
> BERT>  The use case we are looking at currently has hourly peaks of about
> 450K messages per second.  For sizing we want to make sure we can support
> 900K .  Our larger feed in terms of size peaks at 450MBsec so we want to
> make sure the cluster we build can support 900MBsec
>
> BOB> I believe LinkedIn has reported getting a throughput of 900k messages
> though a 6 node cluster. If you can achieve a flush rate of 100MB/s (which
> is easy for a good RAID setup) having a 12 node cluster should be doable.
> Remember when your topic/partition leadership is balance across the cluster
> (preferred replica election) you get to take advantage of all the brokers.
> Don't forget to architect for a failures. Can your cluster handle max
> throughput with two Kafka broker in an offline state?
>
> BERT>  Are you implying that the number of topics has direct correlation to
> the fail-over time?  I think I might test this by creating one topic
> loading 500 million rows and test failover and compare to 500 topics with 1
> million rows each.  Not sure if data in the Q impacts the failover so
> figured I would test that also.
>
> BOB> Yes, that is what we have seen. The current controller architecture
> takes longer for Kafka nodes to fail over. It's not the # of topics, but
> the # of topic/partitions that have to move over. When a Kafka broker fails
> (planed or unplanned), the producers and the consumers have to pause for
> all the topic/partition pairs that were the leader for the off line Kafka
> broker and they have to move to another Kafka broker that is in ISR. By
> having lots of topics/partitions (we have many thousands), it can take a
> bit. Remember it's only a chunk, not all topics and partitions. This of
> course can change as the Kafka development team changes how this works. I
> highly recommend creating your topic and partition counts in DEV/QA and
> test this out. You will see a difference.
>
> As for the amout of data in the topic/partition that is of no concern for
> failover. The Kafka broker will only failover those topics/partitions that
> are in ISR. Replication time once a Kafka broker is brought back online
> will depend on how far behind the Kafka broker is from the leader. This is
> delta in the offset. Planned shutdowns can be minutes, unplanned
> shutdowns/failures can take hours for our data to re-replicate.
>
>
> BERT>  Our default config config has a 256GB of memory also.  One thing I
> do want to test is impact on cluster of reading data not in memory.  Have
> you done any testing like this?
>
> BOB> Yes, it's about putting enough data to flush outside the OS file
> cache. But 512GB of data in your topics to make sure the data is not in the
> cache. Also, you can reset and/or use new consumer groups and make sure you
> read from the lowest-offset. Watch your iostats to see if you get lots of
> reads. On a normal Kafka cluster that is reading cached memory (for
> consumption), you will not see read IO. Assuming you don't have other
> processes on the system reading data (such as log aggregation). We see 30MB
> writes/flushes ever-other-second with 1-2% IO utilization.
>
>
> BERT>  We have not determined what to use just yet for monitoring.  What
> are you guys using?
>
> BOB> We are using a commercial APM solution. It's an java agent the plugs
> into the JVM on boot time. This reads the JMX information as well as file
> I/O rates, NIO rates and GC. It sends to a centralized monitoring console.
> Google "Java APM" for some ideas.
>
>
> BERT>  Can you share more about your config?  Are you using RAID10 or
> RAID5?  What size and speed of drives?  Have you needed to do a RAID
> rebuild and if so did it negatively impact the cluster.   The standard
> server I was given has 12 x 4TB 7.2K drives.  I will either run in JBOD or
> as RAID10.  Parity based RAID with 4TB drives makes me nervous.  I am not
> worried about performance when things are working as designed...we need to
> plan for edge cases when consumer is reading old data or the system needs
> to play catch up on a big backlog.
>
> BOB> We are using RAID10. It was a requirement from our Unix guys. The
> rationale for this was we didn't want to lose just a disk and to have to
> rebuild/re-replicate 20TB of data. We haven't experienced any drive
> failures that I am aware of. We have had complete server failures, but the
> data was still good. I believe we have 10-4TB drives in a RAID10
> configuration. I/O performance is very good.
>
> BERT>  Need to spend some time on zookeeper.   I have not looked at
> zookeeper performance to see if its negatively impacting the performance
> tests I am doing. We haven't  spent any time looking at zookeeper.  Did you
> find that the  SSD helped improve kafka performance?
>
> BOB> We started with SSD. Kafka brokers itself doesn't write a lot of data
> frequently (to zookeeper). It's really about how your consumers flush their
> offsets. This is assuming you will be using the high-level consumer client.
> If you are going to flush the offsets to zookeeper on every message
> consumed (to get best effort nearly-exactly-once processing). You will
> being writing a lot of data to zookeeper. On our 5 node zookeeper cluster,
> we are doing 300+ writes per second, and can spike up to many 1000's.
> Typically it's 1-2MBs data rate. The SSDs are under 2% I/O utilization.
> 200MB of ZK data, and we clean up the files once per hour. We run some
> consumers in batch and flush on time delay. Other consumers are flush per
> message processed. It's the flush per message that causes the high-volume.
>
> Push back on DEVs and software architecture if they want to flush per
> message. Do it where it's only absolutely necessary. :)
>
> The high level Kafka consumer is good at "at least once" processing.
> Exactly once is a harder nut to crack. Exactly once processing may require
> some custom code around the low-level Kafka consumer client.
>
> - Bob
>
>
>
> -----Original Message-----
> From: bertcord@gmail.com [mailto:bertcord@gmail.com] On Behalf Of Bert
> Corderman
> Sent: Thursday, April 17, 2014 7:21 AM
> To: users@kafka.apache.org
> Subject: Re: Cluster design distribution and JBOD vs RAID
>
> Hey Bob,
>
> thanks for your detailed response.  I have added comments inline.
>
>
> On Wed, Apr 16, 2014 at 7:41 PM, Bello, Bob <Bob.Bello@dish.com> wrote:
>
> > Perhaps as you consider the size of your cluster, a few questions about
> > the kind of messaging you are looking at? I can use an example of what we
> > do in our production environment while not going into specifics. These
> are
> > just observations from an OPS perspective. (sorry for the wall of text.)
> >
> > * Size of messages (<100 bytes, <1kB, <10kB, <100kB, <1MB, <10MB,
etc).
> > (we run messages size between a few byes to over 100KB with a few at over
> > 1MB).
> >
> BERT> We have several uses cases we are looking at kafka for.  Today we are
> just using the file system to buffer data between our systems.  We are
> looking at uses cases that have varying message sizes of 200, 300, 1000,
> 2200 bytes
>
> >
> > * Volume of messages per second (we produce over 15k per second and can
> > consume over 100K per second when we are processing though some lag)
> >
> BERT>  The use case we are looking at currently has hourly peaks of about
> 450K messages per second.  For sizing we want to make sure we can support
> 900K .  Our larger feed in terms of size peaks at 450MBsec so we want to
> make sure the cluster we build can support 900MBsec
>
> >
> > * # of Producer clients (a few, a lot) (we have over 300 app servers the
> > produce messages to the Kafka cluster)
> > ** Not only does this affect Kafka broker performance but it can use a
> lot
> > of TCP connections specially if you run a large Kafka cluster
> >
> BERT> our producer count will be low ...maybe 8-16 hosts.
>
> >
> > * # of Consumer clients (a few, a lot) (we have less than 50 app servers
> > that consume at this time)
> > ** This also affects the # of TCP connections to Kafka brokers. (We have
> > over 2400+ TCP connections to our cluster)
> >
> BERT>  This will be much higher but not sure yet.  We are also looking at
> replacing some legacy technology with storm so this is a bit up in the air
> right now.
>
> >
> > * Will you compress your message before sending them to Kafka? (we have a
> > mix of snappy, gzip and non-compressed messages depending on the
> > application). This can affect your disk usage
> >
> BERT> We will use whatever performs best ;)  My gut is that we will be
> using snappy
>
> >
> > * Planned retention period. Longer retention period = more storage
> > required. (we have varied retention periods per topic, between 10 days
> and
> > 30 days).
> >
> > * The number of topics per cluster. I believe Kafka scales well with the
> > number of topics, however you have to worry about a few things:
> > ** More topics, means slower migration/failover when Kafka brokers are
> > shutdown or fail. This has caused us time out issues. Planned shutdown
> of a
> > Kafka broker can take over 30 seconds to over 3 minutes. (We have over
> >10
> > and <50 topics. We are growing topics rapidly.)
> >
> BERT>  Are you implying that the number of topics has direct correlation to
> the fail-over time?  I think I might test this by creating one topic
> loading 500 million rows and test failover adn compare to 500 topics with 1
> million rows each.  Not sure if data in the Q impacts the failover so
> figured I would test that also.
>
> >
> > * The number of partitions per topic. More partitions per topic = more
> > open file handles, (2 per log file, one for data and one more the index).
> > We run average of 130 partitions. You have to consider your cardinality
> for
> > your messages if order is important. Can you use a key that allows a good
> > distribution across partitions while maintaining order? If all your
> message
> > end up in just a few partitions within the topic then it's harder scale
> the
> > consumption. This all depends on your use case.
> >
> BERT>  We are lucky that order is not critical for our large feeds.
>
> >
> > It might sound like good rationale to scale the # of partitions for a
> > topic to a huge number (for just in case). I think it all depends.
> >
> > * How many consumer threads can consume a single topic? You can't go
> wider
> > than the # of partitions however Kafka clients easily work with a large #
> > of partitions with a few consumer threads.
> >
> > * Producer vs. Consumer size. Is your messaging flow Producer or Consumer
> > heavy. Kafka is awesome and sending data to consumers that use "recent"
> > data. Since Kafka uses memory mapped files, any data from Kafka that is
> in
> > RAM will be very fast. (Our servers have 256GB of ram on them).
> >
> BERT>  Our default config config has a 256GB of memory also.  One thing I
> do want to test is impact on cluster of reading data not in memory.  Have
> you done any testing like this?
>
> >
> > * Size of your cluster vs. the # of replicas. Larger # of Kafka brokers
> > means more chance of failure within the cluster. Same kind of reason why
> > you generally won't see a large RAID5 array. You get one failure before
> you
> > lose data. If you decide to run a large cluster and # of replicas will be
> > important. How much risk are you willing to take? (We run a 6 node
> cluster
> > with a replica factor of 3. We can lose a total of two nodes before
> losing
> > data).
> >
> BERT>  Thanks for the datapoint.  We were also planning to go with
> replication factor of 3
>
> >
> > * Are you running on native iron or virtualized? VM is generally lower
> > performance but can generally spin up new instances faster upon failure.
> We
> > run on native iron so we get excellent performance at the cost of longer
> > lead times to provision new Kafka brokers.
> >
> BERT>  We are big fans of vms...however kafka will be on physical
>
> >
> > * Networking. Are you are running 100mbit, 1gig or 10gib? You can only
> > produce and consume so much data. Larger clusters let you run a total
> > aggregate bandwidth. Don't forget about replication! Topic/partition
> > leaders must replicate to all replica Kafkabrokers (hub/spoke). How long
> > can you wait for replication to occur after a planned or un-planned
> outage?
> > (We run >1Gig).
> >
> BERT> 10gb....so cheap now.  I did cost analysis and found that a single
> 10gb port costs about the same as 2 x 1gig.  Five times the bandwidth and
> less latency makes it no brainer.  If your kafka hosts have multiple nics
> make sure they are using the right port.  This one bit me for a little.
>  (hostname config in the broker config)
>
> >
> > * Monitoring. Large # of Kafka brokers means more to monitor. Do you have
> > a centralized monitoring app? Kafka provides a lot (huge!) JMX
> information.
> > Making sense of it all can take some time.
> >
> BERT>  We have not determined what to use just yet for monitoring.  What
> are you guys using?
>
>
> > * Disk I/O. JBOD vs. RAID. How much are you willing to tolerate failures?
> > Do you have provisioned IO? (We run native iron and local disk in a RAID
> > configuration. It was easier for us to manage a single mount point than a
> > bunch in a JBOD configuration. We rely of local RAID and Kafka
> replication
> > to keep enough copies of our data. We have a large amount of disk
> capacity.
> > We can tolerate large re-replication events due to broker failure without
> > affecting producer or consumer performance.)
> >
> BERT>  Can you share more about your config?  Are you using RAID10 or
> RAID5?  What size and speed of drives?  Have you needed to do a RAID
> rebuild and if so did it negatively impact the cluster.   The standard
> server I was given has 12 x 4TB 7.2K drives.  I will either run in JBOD or
> as RAID10.  Parity based RAID with 4TB drives makes me nervous.  I am not
> worried about performance when things are working as designed...we need to
> plan for edge cases when consumer is reading old data or the system needs
> to play catch up on a big backlog.
>
> >
> > * Disk capacity / Kafka Broker capacity. Depending on your volume,
> message
> > size and retention period, how much disk space will you need? (Using our
> > "crystal ball tech(tm)" we decided over 20TB per Kafka broker would meet
> > our needs. We will probably add Kafka brokers over adding disk as we
> > outgrow this.)
> >
> BERT> I need a crystal ball ;)
>
> >
> > * Separate clusters to keep information separated? Do you have a use case
> > for keeping customer data separate? Compliance use cases such as PCI or
> > SOX? This may be a good reason to keep separate Kafka clusters. I assume
> > that you already will keep separate clusters for DEV/QA/PROD.
> >
> BERT>  yes DEV/QA/PROD completely separate
>
> >
> > * Zookeeper performance - 3 node, 5 node or 7 node. Less nodes, better
> > performance. More nodes, better failure tolerance. We run 5 nodes with
> the
> > transaction logs on SSD. Our ZK update performance is very good.
> >
> BERT>  Need to spend some time on zookeeper.   I have not looked at
> zookeeper performance to see if its negatively impacting the performance
> tests I am doing. We haven't  spent any time looking at zookeeper.  Did you
> find that the  SSD helped improve kafka performance?
>
> >
> > # of partitions per Topic debate:
> > Personally, I'm a proponent of larger # of partitions per topic without
> > going way large. You can add Kafka Brokers to increase capacity and get
> > more performance. However though it's possible to add partitions after a
> > topic is created, it can cause issues with your key hashing depending on
> > your message architecture.
> >
> > * Increasing # of brokers = easy
> > * Increasing the # of partitions in a topic with data in it = hard
> >
> > For us, we will be adding more topics and as we add additional messaging
> > functionality.
> >
> > Example:
> >
> > 130 partitions per topic / 6 brokers = 5 leader partitions per broker per
> > topic. If you replicate 3 the you will end up with 3x active partitions
> per
> > broker.
> >
> > 1024 partitions per topic / 24 brokers =~ 43 leader partitions per broker
> > per topic.
> >
> BERT> Thanks for the example.   Good to see others are using larger
> partition counts.
>
> >
> >
> > Final thoughts:
> >
> > There's no magical formula for this as already stated in the wiki. It is
> a
> > lot of trial and error. I will say that we went from a few 100 messages
> per
> > second volume to over 40k per second by adding one application and our
> > Kafka cluster didn't even blink.
> >
> > Kafka is awesome.
> >
> > Btw, we're running 0.8.0.
> >
> >
> >
> > - Bob
> >
> > -----Original Message-----
> > From: bertcord@gmail.com [mailto:bertcord@gmail.com] On Behalf Of Bert
> > Corderman
> > Sent: Wednesday, April 16, 2014 11:58 AM
> > To: users@kafka.apache.org
> > Subject: Cluster design distribution and JBOD vs RAID
> >
> > I am wondering what others are doing in terms of cluster separation. (if
> at
> > all)  For example let’s say I need 24 nodes to support a given workload.
> > What are the tradeoffs between a single 24 node cluster vs 2 x 12 node
> > clusters for example.  The application I support can support separation
> of
> > data fairly easily as the data is all processed in the same way but can
> be
> > sharded isolated based on customers.  I understand the standard
> tradeoffs,
> > for example putting all your eggs in one basket but curious as if there
> are
> > any details specific to Kafka in terms of cluster scale out.
> >
> >
> >
> > Somewhat related is the use of RAID vs JBOD, I have reviewed the
> documents
> > on the Kafka site and understand the tradeoff between space as well as
> > sequential IO vs random and the fact a RAID rebuild might kill the
> system.
> > I am specifically asking the question as it relates to larger cluster and
> > the impact on the number of partitions a topic might need.
> >
> >
> >
> > Take an example of a 24 node cluster with 12 drives each the cluster
> would
> > have 288 drives.  To ensure a topic is distributed across all drives a
> > topic would require 288 partitions.  I am planning to test some of this
> but
> > wanted to know if there was a rule of thumb.  The following link
> >
> >
> https://cwiki.apache.org/confluence/display/KAFKA/FAQ#FAQ-HowdoIchoosethenumberofpartitionsforatopic
> > ?
> > Talks about supporting up to 10K partitions but its not clear if this is
> > for a cluster as a whole vs topic based
> >
> >
> > Those of you running larger clusters what are you doing?
> >
> >
> > Bert
> >
>

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