I see....
A few suggestions:
- Pump enough messages into Kafka that the entire test takes at least more
than a few minutes
- If the test is very short, results can often be misleading
- Use sufficiently large queue sizes
- Generally the lighter the tasks are, the longer queue you need to
avoid worker starvation. Since your task looks very light weight, the
default queue size may not be sufficient. There are a few places to tune.
Have a look at this article:
http://www.michael-noll.com/blog/2013/06/21/understanding-storm-internal-message-buffers/
- Make the task a bit more heavy weight
- Getting higher throughput through parallelising is generally hard
when the tasks are very light weight. If you make each tasks a bit more
heavy weight (e.g. by generating more random numbers per invocation), it
may be easier to observe the performance increase (this way you may also be
able to avoid the message buffer tuning thing mentioned above)
- Try batched topologies
- Rationale is similar to above because batching will effectively make
tasks more "heavy weight".
On Sat, Jul 25, 2015 at 5:23 PM, Dimitris Sarlis <[email protected]>
wrote:
> Inside my bolts I ack every tuple I receive for processing. Furthermore,
> I've set the TOPOLOGY_MAX_SPOUT_PENDING to 1000 to throttle how many tuples
> remain pending at a given moment. The bolts don't have any HashMap or other
> data structure. In fact, my code is pretty simple:
>
> public void execute(Tuple tuple) {
> if (!tuple.getString(0).contains("!")) {
> Random ran = new Random();
> int worker = ran.nextInt(boltNo) + 1;
> List<Integer> l = _topo.getComponentTasks("worker" + worker);
> String out = tuple.getString(0) + "!";
> _collector.emitDirect(l.get(0), new Values(out));
> }
> else {
> LOG.info("Already processed record: " + tuple.getString(0));
> }
> _collector.ack(tuple);
> }
>
> As far as the slowdown is concerned, the rate of data processing is
> constant from start to end. But when I increase the number of spouts/bolts,
> the system takes more time to process the same amount of records (500000
> records to be exact). So, the throughput drops because the rate of data
> digestion is smaller. This is consistent as I increase the number of
> workers, I have tested till 20 spouts/ 20 bolts.
>
> I set the number of Kafka partitions exactly equal to the number of spouts
> each time to take advantage of the parallelism offered.
>
> I don't know if it helps, but I also tried a simpler topology, where bolts
> are not connected with each other (so it's basically a tree like topology)
> and I observed the same drop in throughput but not to the same extent. The
> increase in processing time is almost negligible.
>
>
> On 25/07/2015 07:08 μμ, Enno Shioji wrote:
>
> My first guess would be that there is something with the topology, like
> number of pending messages increasing abnormally due to tuples not being
> acked/too many tuples generated, JVM heap shortage caused on workers due to
> something being retained in bolts (like an ever growing HashMap) etc.
>
> Does the slowdown happen gradually, or is it immediately slow?
>
> Another random guess is that you don't have enough number of Kafka
> partitions, hence not using your new workers but I wouldn't expect this to
> make things significantly slower.
>
>
>
> On Sat, Jul 25, 2015 at 4:48 PM, Dimitris Sarlis <[email protected]>
> wrote:
>
>> Morgan,
>>
>> I hardly think that this could be the problem. The topology is deployed
>> over a 14 node cluster with 56 total cores and 96GB RAM. So when I jump
>> from 8 to 16 workers, I think I am still far below my hardware limitations.
>>
>>
>> On 25/07/2015 06:45 μμ, Morgan W09 wrote:
>>
>> It could be possible that you're reaching a hardware limitation. The jump
>> from 8 to 16 total bolt/workers could be more than you hardware can handle
>> efficiently. So it's starting to have to switch out processes and their
>> memory, which can have substantial overhead causing your program to slow
>> down.
>>
>>
>>
>> On Sat, Jul 25, 2015 at 10:36 AM, Dimitris Sarlis <[email protected]>
>> wrote:
>>
>>> Yes, it listens to its own output. For example, if I have two bolts
>>> (bolt1 and bolt2), I perform the following:
>>>
>>> bolt1.directGrouping("bolt1");
>>> bolt1.directGrouping("bolt2");
>>> bolt2.directGrouping("bolt1");
>>> bolt2.directGrouping("bolt2");
>>>
>>> I know that this could possibly lead to a cycle, but right now the bolts
>>> I'm trying to run perform the following:
>>> if the inputRecord doesn't contain a "!" {
>>> append a "!"
>>> emit to a random node
>>> }
>>> else {
>>> do nothing with the record
>>> }
>>>
>>> Dimitris
>>>
>>>
>>> On 25/07/2015 06:03 μμ, Enno Shioji wrote:
>>>
>>> > Each bolt is connected with itself as well as with each one of the
>>> other bolts
>>> You mean the bolt listens to its own output?
>>>
>>>
>>>
>>>
>>>
>>> On Sat, Jul 25, 2015 at 1:29 PM, Dimitris Sarlis <[email protected]>
>>> wrote:
>>>
>>>> Hi all,
>>>>
>>>> I'm trying to run a topology in Storm and I am facing some scalability
>>>> issues. Specifically, I have a topology where KafkaSpouts read from a Kafka
>>>> queue and emit messages to bolts which are connected with each other
>>>> through directGrouping. (Each bolt is connected with itself as well as with
>>>> each one of the other bolts). Spouts subscribe to bolts with
>>>> shuffleGrouping. I observe that when I increase the number of spouts and
>>>> bolts proportionally, I don't get the speedup I'm expecting to. In fact, my
>>>> topology seems to run slower and for the same amount of data, it takes more
>>>> time to complete. For example, when I increase spouts from 4->8 and bolts
>>>> from 4->8, it takes longer to process the same amount of kafka messages.
>>>>
>>>> Any ideas why this is happening? Thanks in advance.
>>>>
>>>> Best,
>>>> Dimitris Sarlis
>>>>
>>>
>>>
>>>
>>
>>
>
>
