Skip to main content



JMS Consumer (onMessage()) delay in getting message from Oralce AQ

I have an application where I have implemented Oracle AQ. I ran in to a behavior where average time for processing varied as depicted in graph below:



In above graph when volume of orders was less, average processing time came out to be more whereas when load increased with time, average time for processing got constant and then when volume started declining, time again started increasing.

 I analyzed the behavior and found that there is delay in message consumption after message has been produced to AQ. On further analysis I found that AQjmsListenerWorker goes for sleep if message is not available for consumption and sleep time doubles each time (up to peak limit) if message is not available for consumption. Thus optimizing resource utilization if there is no messages in AQ for consumption.

On enabling (-Doracle.jms.traceLevel=6) diagnostics logs for aq api. 

 I analyzed that Listener thread sleep time doubles till 15000 ms (15 sec), starting with default value 1000 ms, if null message is received from AQ. See below excerpt from logs:

 Thread-7 [Fri Oct 10 15:55:32 IST 2014] AQjmsListenerWorker.dispatchOneMsg:  Received the message: null message
Thread-7 [Fri Oct 10 15:55:32 IST 2014] AQjmsListenerWorker.doSleep:  try to wait for 1000 milliseconds
Thread-8 [Fri Oct 10 15:55:33 IST 2014] AQjmsListenerWorker.dispatchOneMsg:  Received the message: null message
Thread-8 [Fri Oct 10 15:55:33 IST 2014] AQjmsSimpleScheduler.feedData:  Got a null message, the sleep time is doubled to 2000
Thread-7 [Fri Oct 10 15:55:33 IST 2014] AQjmsListenerWorker.doSleep:  try to wait for 2000 milliseconds
...........

 Thread-7 [Fri Oct 10 15:55:47 IST 2014] AQjmsListenerWorker.dispatchOneMsg:  Received the message: null message
Thread-7 [Fri Oct 10 15:55:47 IST 2014] AQjmsSimpleScheduler.feedData:  Got a null message, the sleep time is doubled to 15000
Thread-7 [Fri Oct 10 15:55:47 IST 2014] AQjmsListenerWorker.doSleep:  try to wait for 15000 milliseconds

 So when volume was less sleep time was more.

 reference:- https://community.oracle.com/thread/2535275

 To reduce the sleep time I set below system properties. Thus making minimum start time to 100 ms which will double up to 4000 ms.
-Doracle.jms.minSleepTime=100

-Doracle.jms.maxSleepTime=4000

Sleep time is again set to 0 when a not null message is de-queued.

 Thread-7 [Fri Oct 10 15:59:18 IST 2014] AQjmsListenerWorker.dispatchOneMsg:  Received the message: D3DD9DC7EB894ABC915CE80C180C25D5

Thread-7 [Fri Oct 10 15:59:18 IST 2014] AQjmsSimpleScheduler.feedData:  Got a non null message, the sleep time is reset to 0



Comments

Post a Comment

Popular posts

Unix Server ( Edge Node ) hangs when there are many jobs running on hadoop cluster started from Unix Edge Node.

  When a unix server or an edge node is running lots of jobs (like Spark, Hadoop, or custom batch processes), crashes happen. For example. For example a process might hit a segementation fault, memory issue or ay other runtime issue. By default, if ulimit -c is not 0, the OS will create core dump. Core dump are written to disk and can be very large, sometimes hundreds of MBs or even GBs per process. What we realized was that when multiple processes crash at the same time, the system suddenly tries to write core files to disk. This was leading to DisK I/O spikes. Thus, node was becoming unresponsive. This was also leading CPU spike because OS was handling crash logging. Setting "ulimit -c 0" disables core dumps. This way we lose ability to debug crashes via core dump But, kept production edge nodes stable. On most Linux systems, by default, "core dumps" are written in current working directory of the process that crashes. Linux allows you to change core dump file nam...
Post a Comment
Read more




Spring MongoDB Rest API not returning response in 90 seconds which is leading to client timeout

  We have Spring Boot  Rest API deployed in Kubernetes cluster which integrates with MongoDB to fetch the data.  MongoDB is fed with data by a real time Spark & NiFi job.  Our clients complained that for a request what they send they don't have response within 90 seconds. Consider it like an OMS ( Order ManagEment System).  On further analysis, we found that Spark & NiFi processing is happenning within 10 seconds after consuming response data from Kafka. Thus, initally out thought was that it due to delay from upstream to produce data in to Kafka.  Thankfully, our data had create / request  timestamp, and when response was received, and when response was inserted into MongoDB. Subtracting response insert time from request time seemed to be well within 90 seconds. But, still client did timeout on not seeing a response within 90 seconds. This led to confusion on our side.  But, then we realized it was due to Read Preference . We updated this...
Post a Comment
Read more




MongoDB Regex Query taking more time in Production but same query perform well in UAT

   We came across a situation where-in, MongoDB Query was taking more time in Production like 10 seconds and 4.2 seconds but same query performed well in UAT taking under 400 ms. The very first thought that was evident to us that it is because of amount of data which differed in UAT and Production. Then we ran following to see the execution plan -   db.collection.aggregate(<queries>).explain() This gave us Winning and Rejected Plans. Under which, we analyzed that although it was using 'IXSCAN.' But, it was incorrect index- as we had one compound index built on time field and other fields, and there was other index just on time field for TTL purposes. Winning plan picked TTL index rather than compound index. Thus, we dropped TTL index and built TTL index on a different time field.  That got our query performance time from 10 seconds to 726 ms. Also, for other query the performance came down from 8 seconds to 4.3 seconds. Then, we ran following -  ...
Post a Comment
Read more




Machine Learning Part 1

Machine learning uses algorithms to find patterns in data, and then uses a model that recognizes those patterns to make predictions on new data. Machine learning may be broken down into - Supervised learning algorithms use labeled data - Classification, Regression. Unsupervised learning algorithms find patterns in unlabeled data - Clustering, Collaborative Filtering, Frequent Pattern Mining Semi-supervised learning uses a mixture of labeled and unlabeled data. Reinforcement learning trains algorithms to maximize rewards based on feedback. Classification - Mailing Servers like Gmail uses ML to classify if an email is Spam or not based on the data of an email: the sender, recipients, subject, and message body. Classification takes a set of data with known labels and learns how to label new records based on that information. For example- An items is important or not. A transaction is fraud or not based upon known labeled examples of transactions which were classified...
Post a Comment
Read more




Clone multiple projects or repositories from GitLab under a folder or subfolder.

  Suppose we have a group named as groupA in gitlab, under which we have sub folder structure, and there are multiple projects under a folder.  We wish to clone all the projects under folder. This can be done using gitlab api. Like below -  1) You should have Token for authentication. TOKEN="J612a-xUoMxxcerssRe31_" 2) API URL to subfolder that should give you group id.  API_URL1="https://<gitlab.server.com>/api/v4/groups?search=groupA /dir1/dir2/dir3/dir4" 3) Use below to get group id - GROUP_ID=$(curl --silent --header "Private-Token: $TOKEN" "$API_URL1" | jq -r '.[].id') 4) Now you can fetch and clone all projects by using below - API_URL2="https://<gitlab.server.com>/api/v4/groups/$GROUP_ID/projects" # Fetch repositories list REPOS=$(curl --silent --header "Private-Token: $TOKEN" "$API_URL2" | jq -r '.[].http_url_to_repo') # Clone each repository for REPO in $REPOS; do     git clone $RE...
Post a Comment
Read more