I'm using SingleStore to load events from Kafka. I created a Kafka pipeline with the following script:
create pipeline `events_stream`
as load data kafka 'kafka-all-broker:29092/events_stream'
batch_interval 10000
max_partitions_per_batch 6
into procedure `proc_events_stream`
fields terminated by '\t' enclosed by '' escaped by '\\'
lines terminated by '\n' starting by '';
And SingleStore failing with OOM error like the following:
Memory used by MemSQL (4537.88 Mb) has reached the 'maximum_memory' setting (4915 Mb) on this node. Possible causes include (1) available query execution memory has been used up for table memory (in use table memory: 71.50 Mb) and (2) the query is large and complex and requires more query execution memory than is available
I'm a quite confused why 4Gb is not enough to read Kafka by batches....
Is it possible to configure batch_size for the pipeline to avoid memory issues and make the pipeline more predictable?
unfortunately, current version of Singlestore's pipelines only has a global batch size and can not be set individually in stored procedures.
However, In general, each pipeline batch has some overhead, so running 1 batch for 10000 messages should be better in terms of total resources than 100 or even 10 batches for the same number of messages. If the stored procedure is relatively light, the time delay you are experiencing is likely dominated by the network download of 500mb.
It is worth checking if 10000 large messages are arriving on the same partition or several. In Singlestore's pipelines, each database partition downloads messages from a different kafka partition, and that helps parallelize the workload. However, if the messages arrive on just one partition, then you are not getting the benefits of parallel execution.
Please help me also with adding PROPER TAGS
I have a script that streams documents from mongo. It processes them one by one.
Here comes the problem. Processing a document takes some while. If running on 1 processor it's fine. But from 2+ processors, they can be reprocessing the same data. The processors will be added/removed dynamically depending on how our CI is busy.
How can I distribute the docs between the processors?
My ideas:
Distribution based on last digits of doc._id.
Whenever a processor is connected it writes a log to db. From the log other processors would calculate the ranges they should process. The problem is that the script is batch, say 500 docs per call.
Thanks for any ideas.
Below is the configuration:
2 JBoss application nodes
5 listeners on the application node with 50 threads each, supports
clustering and is set up as active-active listener, so they run on
both app nodes
The listener simply gets the message and logs the information into
database
50000 messages are posted into ActiveMQ using JMeter.
Here is the observation on first execution:
Total 50000 messages are consumed in approx 22 mins.
first 0-10000 messages consumed in 1 min approx
10000-20000 messages consumed in 2 mins approx
20000-30000 messages consumed in 4 mins approx
30000-40000 messages consumed in 6 mins approx
40000-50000 messages consumed in 8 mins
So we see the message consumption time is increasing with increasing number of messages.
Second execution without restarting any of the servers:
50000 messages consumed in 53 mins approx!
But after deleting data folder of activemq and restarting activemq,
performance again improves but degrades as more data enters the queue!
I tried multiple configuration in activemq.xml, but no success...
Anybody faced similar issue, and got any solution ? Let me know. Thanks.
I've seen similar slowdowns in our production systems when pending message counts go high. If you're flooding the queues then the MQ process can't keep all the pending messages in memory, and has to go to disk to serve a message. Performance can fall off a cliff in these circumstances. Increase the memory given to the MQ server process.
Also looks as though the disk storage layout is not particularly efficient - perhaps having each message as a file in a single directory? This can make access time rise as traversing disk directory takes longer.
50000 messages in > 20 mins seems very low performance.
Following configuration works well for me (these are just pointers. You may already have tried some of these but see if it works for you)
1) Server and queue/topic policy entry
// server
server.setDedicatedTaskRunner(false)
// queue policy entry
policyEntry.setMemoryLimit(queueMemoryLimit); // 32mb
policyEntry.setOptimizedDispatch(true);
policyEntry.setLazyDispatch(true);
policyEntry.setReduceMemoryFootprint(true);
policyEntry.setProducerFlowControl(true);
policyEntry.setPendingQueuePolicy(new StorePendingQueueMessageStoragePolicy());
2) If you are using KahaDB for persistence then use per destination adapter (MultiKahaDBPersistenceAdapter). This keeps the storage folders separate for each destination and reduces synchronization efforts. Also if you do not worry about abrupt server restarts (due to any technical reason) then you can reduce then disk sync efforts by
kahaDBPersistenceAdapter.setEnableJournalDiskSyncs(false);
3) Try increasing the memory usage, temp and storage disk usage values at server level.
4) If possible increase prefetchSize in prefetch policy. This will improve performance but also increases the memory footprint of consumers.
5) If possible use transactions in consumers. This will help to reduce the message acknowledgement handling and disk sync efforts by server.
Point 5 mentioned by #hemant1900 solved the problem :) Thanks.
5) If possible use transactions in consumers. This will help to reduce
the message acknowledgement handling and disk sync efforts by server.
The problem was in my code. I had not used transaction to persist the data in consumer, which is anyway bad programming..I know :(
But didn't expect that could have caused this issue.
Now 50000, messages are getting processed in less than 2 mins.
Scenario: I have a low-volume topic (~150msgs/sec) for which we would like to have a
low propagation delay from producer to consumer.
I added a time stamp from a producer and read it at consumer to record the propagation delay, with default configurations the msg (of 20 bytes) showed a propagation delay of 1960ms to 1230ms. No network delay is involved since, I tried on a 1 producer and 1 simple consumer on the same machine.
When I have tried adjusting the topic flush interval to 20ms, it drops
to 1100ms to 980ms. Then I tried adjusting the consumers "fetcher.backoff.ms" to 10ms, it dropped to 1070ms - 860ms.
Issue: For a 20 bytes of a msg, I would like to have a propagation delay as low as possible and ~950ms is a higher figure.
Question: Anything I am missing out in configuration?
I do welcome comments, delay which you got as minimum.
Assumption: The Kafka system involves the disk I/O before the consumer get the msg from the producer and this goes with the hard disk RPM and so on..
Update:
Tried to tune the Log Flush Policy for Durability & Latency.Following is the configuration:
# The number of messages to accept before forcing a flush of data to disk
log.flush.interval=10
# The maximum amount of time a message can sit in a log before we force a flush
log.default.flush.interval.ms=100
# The interval (in ms) at which logs are checked to see if they need to be
# flushed to disk.
log.default.flush.scheduler.interval.ms=100
For the same msg of 20 bytes, the delay was 740ms -880ms.
The following statements are made clear in the configuration itself.
There are a few important trade-offs:
Durability: Unflushed data is at greater risk of loss in the event of a crash.
Latency: Data is not made available to consumers until it is flushed (which adds latency).
Throughput: The flush is generally the most expensive operation.
So, I believe there is no way to come down to a mark of 150ms - 250ms. (without hardware upgrade) .
I am not trying to dodge the question but I think that kafka is a poor choice for this use case. While I think Kafka is great (I have been a huge proponent of its use at my workplace), its strength is not low-latency. Its strengths are high producer throughput and support for both fast and slow consumers. While it does provide durability and fault tolerance, so do more general purpose systems like rabbitMQ. RabbitMQ also supports a variety of different clients including node.js. Where rabbitMQ falls short when compared to Kafka is when you are dealing with extremely high volumes (say 150K msg/s). At that point, Rabbit's approach to durability starts to fall apart and Kafka really stands out. The durability and fault tolerance capabilities of rabbit are more than capable at 20K msg/s (in my experience).
Also, to achieve such high throughput, Kafka deals with messages in batches. While the batches are small and their size is configurable, you can't make them too small without incurring a lot of overhead. Unfortunately, message batching makes low-latency very difficult. While you can tune various settings in Kafka, I wouldn't use Kafka for anything where latency needed to be consistently less than 1-2 seconds.
Also, Kafka 0.7.2 is not a good choice if you are launching a new application. All of the focus is on 0.8 now so you will be on your own if you run into problems and I definitely wouldn't expect any new features. For future stable releases, follow the link here stable Kafka release
Again, I think Kafka is great for some very specific, though popular, use cases. At my workplace we use both Rabbit and Kafka. While that may seem gratuitous, they really are complimentary.
I know it's been over a year since this question was asked, but I've just built up a Kafka cluster for dev purposes, and we're seeing <1ms latency from producer to consumer. My cluster consists of three VM nodes running on a cloud VM service (Skytap) with SAN storage, so it's far from ideal hardware. I'm using Kafka 0.9.0.0, which is new enough that I'm confident the asker was using something older. I have no experience with older versions, so you might get this performance increase simply from an upgrade.
I'm measuring latency by running a Java producer and consumer I wrote. Both run on the same machine, on a fourth VM in the same Skytap environment (to minimize network latency). The producer records the current time (System.nanoTime()), uses that value as the payload in an Avro message, and sends (acks=1). The consumer is configured to poll continuously with a 1ms timeout. When it receives a batch of messages, it records the current time (System.nanoTime() again), then subtracts the receive time from the send time to compute latency. When it has 100 messages, it computes the average of all 100 latencies and prints to stdout. Note that it's important to run the producer and consumer on the same machine so that there is no clock sync issue with the latency computation.
I've played quite a bit with the volume of messages generated by the producer. There is definitely a point where there are too many and latency starts to increase, but it's substantially higher than 150/sec. The occasional message takes as much as 20ms to deliver, but the vast majority are between 0.5ms and 1.5ms.
All of this was accomplished with Kafka 0.9's default configurations. I didn't have to do any tweaking. I used batch-size=1 for my initial tests, but I found later that it had no effect at low volume and imposed a significant limit on the peak volume before latencies started to increase.
It's important to note that when I run my producer and consumer on my local machine, the exact same setup reports message latencies in the 100ms range -- the exact same latencies reported if I simply ping my Kafka brokers.
I'll edit this message later with sample code from my producer and consumer along with other details, but I wanted to post something before I forget.
EDIT, four years later:
I just got an upvote on this, which led me to come back and re-read. Unfortunately (but actually fortunately), I no longer work for that company, and no longer have access to the code I promised I'd share. Kafka has also matured several versions since 0.9.
Another thing I've learned in the ensuing time is that Kafka latencies increase when there is not much traffic. This is due to the way the clients use batching and threading to aggregate messages. It's very fast when you have a continuous stream of messages, but any time there is a moment of "silence", the next message will have to pay the cost to get the stream moving again.
It's been some years since I was deep in Kafka tuning. Looking at the latest version (2.5 -- producer configuration docs here), I can see that they've decreased linger.ms (the amount of time a producer will wait before sending a message, in hopes of batching up more than just the one) to zero by default, meaning that the aforementioned cost to get moving again should not be a thing. As I recall, in 0.9 it did not default to zero, and there was some tradeoff to setting it to such a low value. I'd presume that the producer code has been modified to eliminate or at least minimize that tradeoff.
Modern versions of Kafka seem to have pretty minimal latency as the results from here show:
2 ms (median)
3 ms (99th percentile)
14 ms (99.9th percentile)
Kafka can achieve around millisecond latency, by using synchronous messaging. With synchronous messaging, the producer does not collect messages into a patch before sending.
bin/kafka-console-producer.sh --broker-list my_broker_host:9092 --topic test --sync
The following has the same effect:
--batch-size 1
If you are using librdkafka as Kafka client library, you must also set socket.nagle.disable=True
See https://aivarsk.com/2021/11/01/low-latency-kafka-producers/ for some ideas on how to see what is taking those milliseconds.
We have an event processing system that will process events sent directly from the source to handler process at 200 eps (events per second). The queues and message sends are transactional. Adding the NSB distributor between the event generator and the handler process reduces this rate from 200 eps to 70 eps. The disk usage and CPU on the distributor box become significantly higher as well.
Seen with commercial build of NServiceBus, version 2.6.0.1505.
Has anyone else seen this behaviour or have any advice?
One thing you can play with is where MSDTC is located. You can have your workers use the same MSDTC as the distributor, therefore downgrading the level of the transaction and speeding up commits. I would recommend if you do this that you cluster MSDTC to protect against failures.
Assuming you are operating on a DB you could shard your databases to work on different sets of data. You could also move the DB(s) closer to the workers(to the same machine).
I would also check into the settings of your DB provider and MSMQ as there are a few things to tweak there in terms of timeouts and such. Note that there is a trade off when applying certain settings, but it sounds like you'd prefer the quickest throughput.
There are lots of other system level things to check, I'll assume you've been through all those items(network/disk/RAM/etc).