I have configured my Producer with request.timeout.ms = 70,0000ms and retries=5. I have doubt how this actually works,
After this "request.timeout.ms=70,000" expires it retries for 5 times or within given "request.timeout.ms=70,000" it retries for 5 time with retry.backoff.ms value.?
There are 3 important configs to be aware of:
"request.timeout.ms" - time to retry a single request
"delivery.timeout.ms" - time to complete the entire send operation
"retries" - how many times to retry when the broker responds with retriable errors.
The Apache Kafka recommendation is to set "delivery.timeout.ms" and leave the other two configurations with their default value. The idea is that the main thing you as a user should worry about is how long you want to way for Kafka to figure things out before giving up on it. It doesn't really matter what is taking Kafka so long - the connection, getting metadata, long queues, etc, the only thing that matters is how long you are willing to wait.
Now to your question - request.timeout.ms applies on each retry. So Producer will send the recordbatch to Kafka, and if there's no response after 70,000ms it will consider this a failure and retry. Note that most errors (say, NoLeaderForPartition) will return from the broker much faster (which is why retry backoffs are needed).
Reasoning about delivery times with retries + request.timeout.ms turned out to be near impossible even for those who wrote the producer. Hence, the introduction of delivery.time.ms with a very clear contract.
Related
I want to limit my Kafka Consumer message consumption rate to 1 Message per 10 seconds .I'm using kafka streams in Spring boot .
Following is the property I tried to Make this work but it didn't worked out s expected(Consumed many messages at once).
config.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, brokersUrl);
config.put(StreamsConfig.APPLICATION_ID_CONFIG, applicationId);
config.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, autoOffsetReset);
//
config.put(ConsumerConfig.MAX_POLL_RECORDS_CONFIG,1);
config.put(ConsumerConfig.MAX_POLL_INTERVAL_MS_CONFIG, 10000);
is there any way to Manually ACK(Manual offsetCommits) in KafkaStreams? which will be usefull to control the msg consumption rate .
Please note that i'm using Kstreams(KafkaStreams)
Any help is really appreciated . :)
I think you misunderstand what MAX_POLL_INTERVAL_MS_CONFIG actually does.
That is the max allowed time for the client to read an event.
From docs
controls the maximum time between poll invocations before the consumer will proactively leave the group (5 minutes by default). The value of the configuration request.timeout.ms (default to 30 seconds) must always be smaller than max.poll.interval.ms(default to 5 minutes), since that is the maximum time that a JoinGroup request can block on the server while the consumer is rebalance
"maximum time" not saying any "delay" between poll invocations.
Kafka Streams will constantly poll; you cannot easily pause/start it and delay record polling.
To read an event every 10 seconds without losing consumers in the group due to lost heartbeats, then you should use Consumer API, with pause() method, call Thread.sleep(Duration.ofSeconds(10)), then resume() + poll() while setting max.poll.records=1
Finally ,I achieved the desired message consuming limit using Thread.sleep().
Since , there is no way to control the message consumption rate using kafka config properties itself . I had to use my application code to control the rate of consumption .
Example: if I want control the record consumption rate say 4 msg per 10 seconds . Then i will just consumer 4 msg (will keep a count parallely) once 4 records are consumer then i will make the thread sleep for 10 seconds and will repeat the same process over again .
I know it's not a good solution but there was no other way.
thank you OneCricketeer
We have been facing an issue where message rate of a xmitq is very slow comparing with what should be a normal performance.
We have many other Qmgrs with bigger MQ flows that don't have the same issue.
Our HUB qmgr connects to business line in the same company HUB qmgr, and even the destination queues on their side being empty the flow is really slow.
At OS and Network level they say nothing can be done. At MQ we have changed the Buffersizes so it matches the OS level and uses the system tcp windows.
Now at MQ level we have the channel SDR setup with BATCHSZ to 100, but seems the receiver is configured with 30.
We noticed that because we see messages flow in batches fof 30 messages. Also not sure if that is related but we see the XMITQ havs always 30 uncommited messages.
Our questiong for advice.
Would increase the BATCHSZ parameter on SDR/RCVR help the perfomance?
Is there any other parameter at MQ level that could help it?
DIS CHS(NAME) ALL
AMQ8417: Display Channel Status details.
CHANNEL(QMGRA.QMGRB.T7) CHLTYPE(SDR)
BATCHES(234) BATCHSZ(30)
BUFSRCVD(235) BUFSSENT(6391)
BYTSRCVD(6996) BYTSSENT(14396692)
CHSTADA(2020-04-16) CHSTATI(14.38.17)
COMPHDR(NONE,NONE) COMPMSG(NONE,NONE)
COMPRATE(0,0) COMPTIME(0,0)
CONNAME(159.50.69.38(48702)) CURLUWID(398F3E5EEA43381C)
CURMSGS(30) CURRENT
CURSEQNO(43488865) EXITTIME(0,0)
HBINT(300) INDOUBT(YES)
JOBNAME(000051FC00000001) LOCLADDR(10.185.8.122(54908))
LONGRTS(999999999) LSTLUWID(398F3E5EE943381C)
LSTMSGDA(2020-04-16) LSTMSGTI(14.49.46)
LSTSEQNO(43488835) MCASTAT(RUNNING)
MONCHL(HIGH) MSGS(6386)
NETTIME(2789746,3087573) NPMSPEED(NORMAL)
RQMNAME(QMGRB) SHORTRTS(10)
SSLCERTI(*******************)
SSLKEYDA( ) SSLKEYTI( )
SSLPEER(*******************)
SSLRKEYS(0) STATUS(RUNNING)
STOPREQ(NO) SUBSTATE(RECEIVE)
XBATCHSZ(23,7) XMITQ(QMGRB.X7)
XQTIME(215757414,214033427) RVERSION(08000008)
RPRODUCT(MQMM)
qm.ini:
Log:
LogPrimaryFiles=10
LogSecondaryFiles=10
LogFilePages=16384
LogType=LINEAR
LogBufferPages=4096
LogPath=/apps/wmq/QMGR/log/QMGR/
LogWriteIntegrity=SingleWrite
Service:
Name=AuthorizationService
EntryPoints=13
TCP:
SvrSndBuffSize=0
SvrRcvBuffSize=0
ServiceComponent:
Service=AuthorizationService
Name=MQSeries.UNIX.auth.service
Module=/opt/mqm75/lib64/amqzfu
ComponentDataSize=0
Channels:
MaxChannels=500
UPDATED: 15:41 GMT
Just to update the information, both sides are now with BATCHSZ 100 and seems slightly.
AMQ8417: Display Channel Status details.
CHANNEL(QMGRA.QMGRB.T7) CHLTYPE(SDR)
BATCHES(403) BATCHSZ(100)
BUFSRCVD(405) BUFSSENT(23525)
BYTSRCVD(11756) BYTSSENT(53751066)
CHSTADA(2020-04-17) CHSTATI(15.13.51)
COMPHDR(NONE,NONE) COMPMSG(NONE,NONE)
COMPRATE(0,0) COMPTIME(0,0)
CONNAME(159.50.69.38(48702)) CURLUWID(6D66985E94343410)
CURMSGS(0) CURRENT
CURSEQNO(44115897) EXITTIME(0,0)
HBINT(300) INDOUBT(NO)
JOBNAME(0000172A00000001) LOCLADDR(10.185.8.122(2223))
LONGRTS(999999999) LSTLUWID(6D66985E93343410)
LSTMSGDA(2020-04-17) LSTMSGTI(15.30.06)
LSTSEQNO(44115897) MCASTAT(RUNNING)
MONCHL(HIGH) MSGS(23505)
NETTIME(101563,480206) NPMSPEED(NORMAL)
RQMNAME(QMGRB) SHORTRTS(10)
SSLCERTI(*************************************)
SSLKEYDA( ) SSLKEYTI( )
SSLPEER(****************************)
SSLRKEYS(0) STATUS(RUNNING)
STOPREQ(NO) SUBSTATE(MQGET)
XBATCHSZ(1,1) XMITQ(QMGRB.X7)
XQTIME(191225,794134) RVERSION(08000008)
RPRODUCT(MQMM)
AMQ8450: Display queue status details.
QUEUE(QMGRB.X7) TYPE(QUEUE)
CURDEPTH(0) IPPROCS(1)
LGETDATE(2020-04-17) LGETTIME(15.30.06)
LPUTDATE(2020-04-17) LPUTTIME(15.30.06)
MEDIALOG(S2488154.LOG) MONQ(LOW)
MSGAGE(0) OPPROCS(9)
QTIME(794134, 191225) UNCOM(NO)
I'll put a few observations in this answer, but based on any further feedback I may add more.
You are running a very old version of the software on the sender side, MQ 7.5 went out of support almost two years ago (April 30 2018). IBM for a cost will provide extended support for an additional three years, so maybe you fall in that group. The 7.5.0.2 maintenance release itself came out in July 11th 2013, so it is almost seven years old at this point. I would strongly suggest you move to a newer version.
Note that MQ v8.0 goes out of support April 30 2020, and IBM just announced a few days ago that MQ v9.0 goes out of support September 30 2021. When you do migrate you should go with either 9.1 which has no announced end of support (they give five years minimum so it could be 2023) or go with the next version of MQ that should be out some time this year.
You mention setting the following:
TCP:
SvrSndBuffSize=0
SvrRcvBuffSize=0
The above setting apply to the SVRCONN end of a client connection. You can see this in the MQ v7.5 Knowledge Center page WebSphere MQ>Configuring>Changing configuration information>Changing queue manager configuration information>TCP, LU62, NETBIOS, and SPX:
SvrSndBuffSize=32768|number
The size in bytes of the TCP/IP send buffer used by the server end of a client-connection
server-connection channel.
SvrRcvBuffSize=32768|number
The size in bytes of the TCP/IP receive buffer used by the server end of a client-connection
server-connection channel.
At IBM MQ v7.5.0.2 APAR IV58073 introduced the concept of setting various buffer settings to a value to 0 which means that it will allow the operating system defaults to be used. Unfortunately like many things in the Knowledge Center it does not look like IBM documented this correctly for 7.5.
You can however review the IBM MQ v8.0 Knowledge Center to get the full picture regarding these settings at the page Configuring>Changing configuration information>Changing queue manager configuration information>TCP, LU62, and NETBIOS, specifically you would want to set these two settings to have any impact on your Sender Channel:
SndBuffSize=number| 0
The size in bytes of the TCP/IP send buffer used by the sending end of
channels. This stanza value can be overridden by a stanza more
specific to the channel type, for example RcvSndBuffSize. If the
value is set as zero, the operating system defaults are used. If no
value is set, then the IBM MQ default, 32768, is used.
RcvSndBuffSize=number| 0
The size in bytes of the TCP/IP send buffer used by the sender end of
a receiver channel. If the value is set as zero, the operating system
defaults are used. If no value is set, then the IBM MQ default, 32768,
is used.
Starting at IBM MQ v8.0 any newly created queue manager will have all of the following in the qm.ini:
TCP:
SndBuffSize=0
RcvBuffSize=0
RcvSndBuffSize=0
RcvRcvBuffSize=0
ClntSndBuffSize=0
ClntRcvBuffSize=0
SvrSndBuffSize=0
SvrRcvBuffSize=0
However, any queue manager that is upgraded will not by default get those settings, meaning if those are not present they will not be added, if they are present they will remain the same. If the setting is not present then as it says above "the IBM MQ default, 32768, is used."
I had extensive discussions with IBM support on this topic and came to the conclusion that they did not see any reason to not set it to 0, they only saw benefit in doing so, but with an abundance of caution they do not change it to 0 for you.
I would recommend you add all of those to your qm.ini, but at minimum add the two I highlighted above.
These are good setting to implement but may not solve your problem if nothing changed recently on either end. If however something did change, for example a network difference, or MQ was upgraded to 8.0.0.8 on the remote side, then this setting just might solve your problem.
In the channel status output two values are interesting:
NETTIME(2789746,3087573)
XQTIME(215757414,214033427)
NETTIME means that based on recent activity it took 2.7 seconds to receive a response from the RCVR channel, over a longer period of time it took 3.1 seconds to receive a response from the RCVR channel. Can you compare this to a TCP ping from the sender channel server to the receive channel server, 2.7 seconds for a response over the network seems excessive. In the presentation Keeping MQ Channels Up and Running given at Capitalware's MQ Technical Conference v2.0.1.4, Paul Clarke who used to work for IBM states "NETTIME only measures network time, and does not include
the MQCMIT for example".
XQTIME means that based on recent activity and over a longer period of time it took ~215 seconds for a message on the XMITQ to be picked up by the SDR channel to be sent.
See below for how IBM documents these:
NETTIME
Amount of time, displayed in microseconds, to send a request to the remote end of the channel and receive a response. This time only measures the network time for such an operation. Two values are displayed:
A value based on recent activity over a short period.
A value based on activity over a longer period.
XQTIME
The time, in microseconds, that messages remained on the transmission queue before being retrieved. The time is measured from when the message is put onto the transmission queue until it is retrieved to be sent on the channel and, therefore, includes any interval caused by a delay in the putting application.
Two values are displayed:
A value based on recent activity over a short period.
A value based on activity over a longer period.
Information on the BATCHSZ channel parameter can be found in the IBM MQ v8.0 Knowledge Center page Reference>Configuration reference>Channel attributes>Channel attributes in alphabetical order>Batch size (BATCHSZ). I have quoted it and highlighted a few areas in bold.
This attribute is the maximum number of messages to be sent before a sync point is taken.
The batch size does not affect the way the channel transfers messages; messages are always transferred individually, but are committed or backed out as a batch.
To improve performance, you can set a batch size to define the maximum number of messages to be transferred between two sync points. The batch size to be used is negotiated when a channel starts, and the lower of the two channel definitions is taken. On some implementations, the batch size is calculated from the lowest of the two channel definitions and the two queue manager MAXUMSGS values. The actual size of a batch can be less; for example, a batch completes when there are no messages left on the transmission queue or the batch interval expires.
A large value for the batch size increases throughput, but recovery times are increased because there are more messages to back out and send again. The default BATCHSZ is 50, and you are advised to try that value first. You might choose a lower value for BATCHSZ if your communications are unreliable, making the need to recover more likely.
This attribute is valid for channel types of:
Sender
Server
Receiver
Requester
Cluster sender
Cluster receiver
Follow up questions:
Are the messages that are PUT to this XMITQ persistent?
Answer: Yes, in our PROD env all messages are pesistent.
Have you had a recent increase in volume going to this XMITQ?
Answer: No, we use a monitoring tools, we extracted a report that show very similar message rate during the period. The same rate over the last 2 weeks.
Do the putting applications set MQPMO_SYNCPOINT and then commit after 1 or more messages are PUT to the queue?
Answer: I will check with the application team.
A couple of things..
You have XBATCHSZ(1,1) so your recent batch size is 1 message per batch.
Total messages 23505 batches 403, so an average of 58 messages per batch. If your recent batch size is 1, then you must have had some larger (100?) batch sizes
XQTIME 191225 is number of microseconds messages were on the xmit queue before being sent. This is 0.1 second!
Nettime 101563 microseconds. This is a long time ( 0.1 seconds) 10,000 would be a good value. Compare this with a "TCP PING"
BUFSSENT 23525 is similar to number of messages - so message size is typically under 32K. Bytessent. messages gives 2286 so small messages.
Things to check
The queue at the remote end. Has it filled up? This would cause the sender queue to get more messages
The nettime seems very long. Compare this with TCP Ping. Nettime can include slow IO at the remote end - or a queue full at the remote end
XQTIME is high. This could be caused by sending applications not committing, or slow disk IO
I wrote "Why is my xmit queue filling up" in this blog
*Search for the title
have a read.
Capture these metrics over a day and see if they are typical
regards
Colin Paice
Trying to understand (new to kafka)how the poll event loop in kafka works.
Use Case : 25 records on the topic, max poll size is set to 5.
max.poll.interval.ms = 5000 //5 seconds by default max.poll.records = 5
Sequence of tasks
Poll the records from the topic.
Process the records in a for loop.
Some processing login where the logic would either pass or fail.
If logic passes (with offset) will be added to a map.
Then it will be committed using commitSync call.
If fails then the loop will break and whatever was success before this would be committed.The problem starts after this.
The next poll would just keep moving in batches of 5 even after error, is it expected?
What we basically expect is that the loop breaks and the offsets till success process message logic should get committed, then the next poll should continue from the failed message.
Example, 1st batch of poll 5 messages polled and 1,2 offsets successful and committed then 3rd failed.So the poll call keep moving to next batch like 5-10,10-15 if there are any errors in between we expect it to stop at that point and poll should start from 3 in first case or if it fails in 2nd batch at 8 then the next poll should start from 8th offset not from next max poll batch settings which would be like 5 in this case.IF IT MATTERS USING SPRING BOOT PROJECT and enable autocommit is false.
I have tried finding this in documentation but no help.
tried tweaking this but no help max.poll.interval.ms
EDIT: Not accepted answer because there is no direct solution for a customer consumer.Keeping this for informational purpose
max.poll.interval.ms is milliseconds, not seconds so it should be 5000.
Once the records have been returned by the poll (and offsets not committed), they won't be returned again unless you restart the consumer or perform seek() operations on the consumer to reset the offset to the unprocessed ones.
The Spring for Apache Kafka project provides a SeekToCurrentErrorHandler to perform this task for you.
If you are using the consumer yourself (which it sounds like), you must do the seeks.
You can manually seek to the beginning offset of the poll for all the assigned partitions on failure. I am not sure using spring consumer.
Sample code for seeking offset to beginning for normal consumer.
In the code below I am getting the records list per partition and then getting the offset of the first record to seek to.
def seekBack(records: ConsumerRecords[String, String]) = {
records.partitions().map(partition => {
val partitionedRecords = records.records(partition)
val offset = partitionedRecords.get(0).offset()
consumer.seek(partition, offset)
})
}
One problem doing this in production is bad since you don't want seekback all the time only in cases where you have a transient error otherwise you will end up retrying infinitely.
I have been testing MongoDB 2.6.7 for the last couple of months using YCSB 0.1.4. I have captured good data comparing SSD to HDD and am producing engineering reports.
After my testing was completed, I wanted to explore the allanbank async driver. When I got it up and running (I am not a developer, so it was a challenge for me), I first wanted to try the rebuilt sync driver. I found performance improvements of 30-100%, depending on the workload, and was very happy with it.
Next, I tried the async driver. I was not able to see much difference between it and my results with the native driver.
The command I'm running is:
./bin/ycsb run mongodb -s -P workloads/workloadb -p mongodb.url=mongodb://192.168.0.13:27017/ycsb -p mongodb.writeConcern=strict -threads 96
Over the course of my testing (mostly with the native driver), I have experimented with more and less threads than 96; turned on "noatime"; tried both xfs and ext4; disabled hyperthreading; disabled half my 12 cores; put the journal on a different drive; changed sync from 60 seconds to 1 second; and checked the network bandwidth between the client and server to ensure its not oversubscribed (10GbE).
Any feedback or suggestions welcome.
The Async move exceeded my expectations. My experience is with the Python Sync (pymongo) and Async driver (motor) and the Async driver achieved greater than 10x the throughput. further, motor is still using pymongo under the hoods but adds the async ability. that could easily be the case with your allanbank driver.
Often the dramatic changes come from threading policies and OS configurations.
Async needn't and shouldn't use any more threads than cores on the VM or machine. For example, if you're server code is spawning a new thread per incoming conn -- then all bets are off. start by looking at the way the driver is being utilized. A 4 core machine uses <= 4 incoming threads.
On the OS level, you may have to fine-tune parameters like net.core.somaxconn, net.core.netdev_max_backlog, sys.fs.file_max, /etc/security/limits.conf nofile and the best place to start is looking at nginx related performance guides including this one. nginx is the server that spearheaded or at least caught the attention of many linux sysadmin enthusiasts. Contrary to popular lore one should reduce your keepalive timeout opposed to lengthen it. The default keep-alive timeout is some absurd (4 hours) number of seconds. you might want to cut the cord in 1 minute. basically, think a short sweet relationship with your clients connections.
Bear in mind that Mongo is not Async so you can use a Mongo driver pool. nevertheless, don't let the driver get stalled on slow queries. cut it off in 5 to 10 seconds using the following equivalents in Java. I'm just cutting and pasting here with no recommendations.
# Specifies a time limit for a query operation. If the specified time is exceeded, the operation will be aborted and ExecutionTimeout is raised. If max_time_ms is None no limit is applied.
# Raises TypeError if max_time_ms is not an integer or None. Raises InvalidOperation if this Cursor has already been used.
CONN_MAX_TIME_MS = None
# socketTimeoutMS: (integer) How long (in milliseconds) a send or receive on a socket can take before timing out. Defaults to None (no timeout).
CLIENT_SOCKET_TIMEOUT_MS=None
# connectTimeoutMS: (integer) How long (in milliseconds) a connection can take to be opened before timing out. Defaults to 20000.
CLIENT_CONNECT_TIMEOUT_MS=20000
# waitQueueTimeoutMS: (integer) How long (in milliseconds) a thread will wait for a socket from the pool if the pool has no free sockets. Defaults to None (no timeout).
CLIENT_WAIT_QUEUE_TIMEOUT_MS=None
# waitQueueMultiple: (integer) Multiplied by max_pool_size to give the number of threads allowed to wait for a socket at one time. Defaults to None (no waiters).
CLIENT_WAIT_QUEUE_MULTIPLY=None
Hopefully you will have the same success. I was ready to bail on Python prior to async
We are using Apache JMeter 2.12 in order to measure the response time of our JMS queue. However, we would like to see how many of those requests take less than a certain time. This, according to the official site of JMeter (http://jmeter.apache.org/usermanual/component_reference.html) should be set by the Timeout property. You can see in the photo below how our configuration looks like:
However, setting the timeout does not result in an error after sending 100 requests. We can see that some of them take apparently more than that amount of time:
Is there some other setting I am missing or is there a way to achieve my goal?
Thanks!
The JMeter documentation for JMS Point-to-Point describes the timeout as
The timeout in milliseconds for the reply-messages. If a reply has not been received within the specified time, the specific testcase failes and the specific reply message received after the timeout is discarded. Default value is 2000 ms.
This is timing not the actual sending the message but receipt of a response.
The source for the JMeter Point to Point will determine if you have a 'Receive Queue' Configured. If you do it will go through the executor path and use the timeout value, otherwise it does not use time timeout value.
if (useTemporyQueue()) {
executor = new TemporaryQueueExecutor(session, sendQueue);
} else {
producer = session.createSender(sendQueue);
executor = new FixedQueueExecutor(producer, getTimeoutAsInt(), isUseReqMsgIdAsCorrelId());
}
In your screen shot JNDI name Receive Queue is not defined, thus it uses temporary queue, and does not use the timeout. Should or should not timeout be supported in this case, that is best discussed in JMeter forum.
Alternately if you want to see request times in percentiles/buckets please read this stack overflow Q/A -
I want to find out the percentage of HTTPS requests that take less than a second in JMeter