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

Popular posts

Spring MongoDB Rename field with derived Value of another field

Input Collection -  [ { 'k' : 'Troubleshooting' , 'hour' : '2024-10-10T16' , 'v' : [ 'WebPage, Login' ] }, { 'k' : 'TroubleshootingMe' , 'hour' : '2024-10-07T01' , 'v' : [ 'Accounts, etc' ] }  ] Expected Output -  [ { 'hour' : '2024-10-10T16' , 'Troubleshooting' : [ 'WebPage, Login' ] }, { 'hour' : '2024-10-07T01' , 'TroubleshootingMe' : [ 'Accounts, etc' ] }  ]   Above Can be achieved by  $replaceRoot / $replaceWith as follows - { $replaceWith : { $mergeObjects : [ { hour : "$hour" }, { "$arrayToObject" : [ [ { k : "$k" , v : "$v" } ] ] } ] } } or { $replaceRoo...




Spark MongoDB Connector Not leading to correct count or data while reading

  We are using Scala 2.11 , Spark 2.4 and Spark MongoDB Connector 2.4.4 Use Case 1 - We wanted to read a Shareded Mongo Collection and copy its data to another Mongo Collection. We noticed that after Spark Job successful completion. Output MongoDB did not had many records. Use Case 2 -  We read a MongoDB collection and doing count on dataframe lead to different count on each execution. Analysis,  We realized that MongoDB Spark Connector is missing data on bulk read as a dataframe. We tried various partitioner, listed on page -  https://www.mongodb.com/docs/spark-connector/v2.4/configuration/  But, none of them worked for us. Finally, we tried  MongoShardedPartitioner  this lead to constant count on each execution. But, it was greater than the actual count of records on the collection. This seems to be limitation with MongoDB Spark Connector. But,  MongoShardedPartitioner  seemed closest possible solution to this kind of situation. But, it per...




Experience with MongoDB and Optimizations

  Experience with MongoDB and Optimizations Before reading below. I would like to point out that this  experience  is related to version  6.0.14-ent, having 6 shards, each shard having 3 machines, each machine is VM with 140 GB RAM and 2TB SSD. And, we had been hosting almost 36 TB of data. MongoDB is not good with Big Data Joins and/ or Big Data OLAP processing. It is mainly meant for OLTP purposes.  Instead of joining millions of keys between 2 collections. It is better to lookup data of one key from one collection then lookup it in other collection. Thus, merging data from 2 collection for same key. Its better to keep De-normalized data in one document.  Updating a document later is cumbersome.  MongoDB crash if data is overloaded. And, it has long downtime if crashed unlike other databases which fails write to database if disk space achieves certain limit. Thus, keeping database active and running for read traffic. MongoDB needs indexes for fast qu...




Spring MongoDB Log Connection Pool Details - Active, Used, Waiting

  We couldn't find any direct way to log Mongo Connection pool Size. So, we did implement an indirect way as below.  This may be incorrect at times when dealing with Sharded MongoDB having Primaty & Secondary nodes. Because, connection may be used based on read prefrence - Primary, primaryPreferred, Secondary, etc. But, this gives an understanding if connections are used efficiently and there is no wait to acquire connections from pool. This can be further enhanced to log correct connection pool statistics.  1) Implement  MyConnectionPoolListener  as below -  import java.util.concurrent.atomic.AtomicInteger; import org.slf4j.Logger; import org.slf4j.LoggerFactory; import com.mongodb.event.ConnectionCheckOutFailedEvent; import com.mongodb.event.ConnectionCheckOutStartedEvent; import com.mongodb.event.ConnectionCheckedInEvent; import com.mongodb.event.ConnectionCheckedOutEvent; import com.mongodb.event.ConnectionClosedEvent; import com.mongodb.event.Conne...




Spark Streaming with Kafka Leading to increase in Open File Descriptors ( Kafka )

  Open File Descriptors w.r.t Kafka brokers relates with following -  number of file descriptors to just track log segment files. Additional file descriptors to communicate via network sockets with external parties (such as clients, other brokers, Zookeeper, and Kerberos). For # 1 this is formula -  (number of partitions)*(partition size / segment size) Reference -  https://docs.cloudera.com/cdp-private-cloud-base/7.1.6/kafka-performance-tuning/topics/kafka-tune-broker-syslevel-file-descriptors.html For #2, every connection made my consumer or producer or zookeeper or  Kerberos  opens file descriptors. Note that each TCP connection creates 2 file descriptors. These connections can be for internal communication of heartbeat, or  security handshake , or data transfer to or from client (producer or consumer) When we run a Spark application integrating it with  Kafka . And, if it is not stable, meaning -  Streaming window for micro batches is les...