Re: Hadoop's Configuration object isn't threadsafe
Hey Andrew, I think you are correct and a follow up to SPARK-2521 will end up fixing this. The desing of SPARK-2521 automatically broadcasts RDD data in tasks and the approach creates a new copy of the RDD and associated data for each task. A natural follow-up to that patch is to stop handling the jobConf separately (since we will now broadcast all referents of the RDD itself) and just have it broadcasted with the RDD. I'm not sure if Reynold plans to include this in SPARK-2521 or afterwards, but it's likely we'd do that soon. - Patrick On Wed, Jul 16, 2014 at 10:24 PM, Andrew Ash and...@andrewash.com wrote: Hi Patrick, thanks for taking a look. I filed as https://issues.apache.org/jira/browse/SPARK-2546 Would you recommend I pursue the cloned Configuration object approach now and send in a PR? Reynold's recent announcement of the broadcast RDD object patch may also have implications of the right path forward here. I'm not sure I fully understand the implications though: https://github.com/apache/spark/pull/1452 Once this is committed, we can also remove the JobConf broadcast in HadoopRDD. Thanks! Andrew On Tue, Jul 15, 2014 at 5:20 PM, Patrick Wendell pwend...@gmail.com wrote: Hey Andrew, Cloning the conf this might be a good/simple fix for this particular problem. It's definitely worth looking into. There are a few things we can probably do in Spark to deal with non-thread-safety inside of the Hadoop FileSystem and Configuration classes. One thing we can do in general is to add barriers around the locations where we knowingly access Hadoop FileSystem and Configuration state from multiple threads (e.g. during our own calls to getRecordReader in this case). But this will only deal with writer writer conflicts where we had multiple calls mutating the same object at the same time. It won't deal with reader writer conflicts where some of our initialization code touches state that is needed during normal execution of other tasks. - Patrick On Tue, Jul 15, 2014 at 12:56 PM, Andrew Ash and...@andrewash.com wrote: Hi Shengzhe, Even if we did make Configuration threadsafe, it'd take quite some time for that to trickle down to a Hadoop release that we could actually rely on Spark users having installed. I agree we should consider whether making Configuration threadsafe is something that Hadoop should do, but for the short term I think Spark needs to be able to handle the common scenario of Configuration being single-threaded. Thanks! Andrew On Tue, Jul 15, 2014 at 2:43 PM, yao yaosheng...@gmail.com wrote: Good catch Andrew. In addition to your proposed solution, is that possible to fix Configuration class and make it thread-safe ? I think the fix should be trivial, just use a ConcurrentHashMap, but I am not sure if we can push this change upstream (will hadoop guys accept this change ? for them, it seems they never expect Configuration object being accessed by multiple threads). -Shengzhe On Mon, Jul 14, 2014 at 10:22 PM, Andrew Ash and...@andrewash.com wrote: Hi Spark devs, We discovered a very interesting bug in Spark at work last week in Spark 0.9.1 -- that the way Spark uses the Hadoop Configuration object is prone to thread safety issues. I believe it still applies in Spark 1.0.1 as well. Let me explain: *Observations* - Was running a relatively simple job (read from Avro files, do a map, do another map, write back to Avro files) - 412 of 413 tasks completed, but the last task was hung in RUNNING state - The 412 successful tasks completed in median time 3.4s - The last hung task didn't finish even in 20 hours - The executor with the hung task was responsible for 100% of one core of CPU usage - Jstack of the executor attached (relevant thread pasted below) *Diagnosis* After doing some code spelunking, we determined the issue was concurrent use of a Configuration object for each task on an executor. In Hadoop each task runs in its own JVM, but in Spark multiple tasks can run in the same JVM, so the single-threaded access assumptions of the Configuration object no longer hold in Spark. The specific issue is that the AvroRecordReader actually _modifies_ the JobConf it's given when it's instantiated! It adds a key for the RPC protocol engine in the process of connecting to the Hadoop FileSystem. When many tasks start at the same time (like at the start of a job), many tasks are adding this configuration item to the one Configuration object at once. Internally Configuration uses a java.lang.HashMap, which isn't threadsafe... The below post is an excellent explanation of what happens in the situation where multiple threads insert into a HashMap at the same time. http://mailinator.blogspot.com/2009/06/beautiful-race-condition.html The gist
Re: Hadoop's Configuration object isn't threadsafe
Sounds good -- I added comments to the ticket. Since SPARK-2521 is scheduled for a 1.1.0 release and we can work around with spark.speculation, I don't personally see a need for a 1.0.2 backport. Thanks looking through this issue! On Thu, Jul 17, 2014 at 2:14 AM, Patrick Wendell pwend...@gmail.com wrote: Hey Andrew, I think you are correct and a follow up to SPARK-2521 will end up fixing this. The desing of SPARK-2521 automatically broadcasts RDD data in tasks and the approach creates a new copy of the RDD and associated data for each task. A natural follow-up to that patch is to stop handling the jobConf separately (since we will now broadcast all referents of the RDD itself) and just have it broadcasted with the RDD. I'm not sure if Reynold plans to include this in SPARK-2521 or afterwards, but it's likely we'd do that soon. - Patrick On Wed, Jul 16, 2014 at 10:24 PM, Andrew Ash and...@andrewash.com wrote: Hi Patrick, thanks for taking a look. I filed as https://issues.apache.org/jira/browse/SPARK-2546 Would you recommend I pursue the cloned Configuration object approach now and send in a PR? Reynold's recent announcement of the broadcast RDD object patch may also have implications of the right path forward here. I'm not sure I fully understand the implications though: https://github.com/apache/spark/pull/1452 Once this is committed, we can also remove the JobConf broadcast in HadoopRDD. Thanks! Andrew On Tue, Jul 15, 2014 at 5:20 PM, Patrick Wendell pwend...@gmail.com wrote: Hey Andrew, Cloning the conf this might be a good/simple fix for this particular problem. It's definitely worth looking into. There are a few things we can probably do in Spark to deal with non-thread-safety inside of the Hadoop FileSystem and Configuration classes. One thing we can do in general is to add barriers around the locations where we knowingly access Hadoop FileSystem and Configuration state from multiple threads (e.g. during our own calls to getRecordReader in this case). But this will only deal with writer writer conflicts where we had multiple calls mutating the same object at the same time. It won't deal with reader writer conflicts where some of our initialization code touches state that is needed during normal execution of other tasks. - Patrick On Tue, Jul 15, 2014 at 12:56 PM, Andrew Ash and...@andrewash.com wrote: Hi Shengzhe, Even if we did make Configuration threadsafe, it'd take quite some time for that to trickle down to a Hadoop release that we could actually rely on Spark users having installed. I agree we should consider whether making Configuration threadsafe is something that Hadoop should do, but for the short term I think Spark needs to be able to handle the common scenario of Configuration being single-threaded. Thanks! Andrew On Tue, Jul 15, 2014 at 2:43 PM, yao yaosheng...@gmail.com wrote: Good catch Andrew. In addition to your proposed solution, is that possible to fix Configuration class and make it thread-safe ? I think the fix should be trivial, just use a ConcurrentHashMap, but I am not sure if we can push this change upstream (will hadoop guys accept this change ? for them, it seems they never expect Configuration object being accessed by multiple threads). -Shengzhe On Mon, Jul 14, 2014 at 10:22 PM, Andrew Ash and...@andrewash.com wrote: Hi Spark devs, We discovered a very interesting bug in Spark at work last week in Spark 0.9.1 -- that the way Spark uses the Hadoop Configuration object is prone to thread safety issues. I believe it still applies in Spark 1.0.1 as well. Let me explain: *Observations* - Was running a relatively simple job (read from Avro files, do a map, do another map, write back to Avro files) - 412 of 413 tasks completed, but the last task was hung in RUNNING state - The 412 successful tasks completed in median time 3.4s - The last hung task didn't finish even in 20 hours - The executor with the hung task was responsible for 100% of one core of CPU usage - Jstack of the executor attached (relevant thread pasted below) *Diagnosis* After doing some code spelunking, we determined the issue was concurrent use of a Configuration object for each task on an executor. In Hadoop each task runs in its own JVM, but in Spark multiple tasks can run in the same JVM, so the single-threaded access assumptions of the Configuration object no longer hold in Spark. The specific issue is that the AvroRecordReader actually _modifies_ the JobConf it's given when it's instantiated! It adds a key for the RPC protocol engine in the process of connecting to the Hadoop FileSystem. When many tasks start at the
Re: Hadoop's Configuration object isn't threadsafe
Hi Patrick, thanks for taking a look. I filed as https://issues.apache.org/jira/browse/SPARK-2546 Would you recommend I pursue the cloned Configuration object approach now and send in a PR? Reynold's recent announcement of the broadcast RDD object patch may also have implications of the right path forward here. I'm not sure I fully understand the implications though: https://github.com/apache/spark/pull/1452 Once this is committed, we can also remove the JobConf broadcast in HadoopRDD. Thanks! Andrew On Tue, Jul 15, 2014 at 5:20 PM, Patrick Wendell pwend...@gmail.com wrote: Hey Andrew, Cloning the conf this might be a good/simple fix for this particular problem. It's definitely worth looking into. There are a few things we can probably do in Spark to deal with non-thread-safety inside of the Hadoop FileSystem and Configuration classes. One thing we can do in general is to add barriers around the locations where we knowingly access Hadoop FileSystem and Configuration state from multiple threads (e.g. during our own calls to getRecordReader in this case). But this will only deal with writer writer conflicts where we had multiple calls mutating the same object at the same time. It won't deal with reader writer conflicts where some of our initialization code touches state that is needed during normal execution of other tasks. - Patrick On Tue, Jul 15, 2014 at 12:56 PM, Andrew Ash and...@andrewash.com wrote: Hi Shengzhe, Even if we did make Configuration threadsafe, it'd take quite some time for that to trickle down to a Hadoop release that we could actually rely on Spark users having installed. I agree we should consider whether making Configuration threadsafe is something that Hadoop should do, but for the short term I think Spark needs to be able to handle the common scenario of Configuration being single-threaded. Thanks! Andrew On Tue, Jul 15, 2014 at 2:43 PM, yao yaosheng...@gmail.com wrote: Good catch Andrew. In addition to your proposed solution, is that possible to fix Configuration class and make it thread-safe ? I think the fix should be trivial, just use a ConcurrentHashMap, but I am not sure if we can push this change upstream (will hadoop guys accept this change ? for them, it seems they never expect Configuration object being accessed by multiple threads). -Shengzhe On Mon, Jul 14, 2014 at 10:22 PM, Andrew Ash and...@andrewash.com wrote: Hi Spark devs, We discovered a very interesting bug in Spark at work last week in Spark 0.9.1 -- that the way Spark uses the Hadoop Configuration object is prone to thread safety issues. I believe it still applies in Spark 1.0.1 as well. Let me explain: *Observations* - Was running a relatively simple job (read from Avro files, do a map, do another map, write back to Avro files) - 412 of 413 tasks completed, but the last task was hung in RUNNING state - The 412 successful tasks completed in median time 3.4s - The last hung task didn't finish even in 20 hours - The executor with the hung task was responsible for 100% of one core of CPU usage - Jstack of the executor attached (relevant thread pasted below) *Diagnosis* After doing some code spelunking, we determined the issue was concurrent use of a Configuration object for each task on an executor. In Hadoop each task runs in its own JVM, but in Spark multiple tasks can run in the same JVM, so the single-threaded access assumptions of the Configuration object no longer hold in Spark. The specific issue is that the AvroRecordReader actually _modifies_ the JobConf it's given when it's instantiated! It adds a key for the RPC protocol engine in the process of connecting to the Hadoop FileSystem. When many tasks start at the same time (like at the start of a job), many tasks are adding this configuration item to the one Configuration object at once. Internally Configuration uses a java.lang.HashMap, which isn't threadsafe... The below post is an excellent explanation of what happens in the situation where multiple threads insert into a HashMap at the same time. http://mailinator.blogspot.com/2009/06/beautiful-race-condition.html The gist is that you have a thread following a cycle of linked list nodes indefinitely. This exactly matches our observations of the 100% CPU core and also the final location in the stack trace. So it seems the way Spark shares a Configuration object between task threads in an executor is incorrect. We need some way to prevent concurrent access to a single Configuration object. *Proposed fix* We can clone the JobConf object in HadoopRDD.getJobConf() so each task gets its own JobConf object (and thus Configuration object). The optimization of broadcasting the Configuration object across the
Re: Hadoop's Configuration object isn't threadsafe
Good catch Andrew. In addition to your proposed solution, is that possible to fix Configuration class and make it thread-safe ? I think the fix should be trivial, just use a ConcurrentHashMap, but I am not sure if we can push this change upstream (will hadoop guys accept this change ? for them, it seems they never expect Configuration object being accessed by multiple threads). -Shengzhe On Mon, Jul 14, 2014 at 10:22 PM, Andrew Ash and...@andrewash.com wrote: Hi Spark devs, We discovered a very interesting bug in Spark at work last week in Spark 0.9.1 — that the way Spark uses the Hadoop Configuration object is prone to thread safety issues. I believe it still applies in Spark 1.0.1 as well. Let me explain: *Observations* - Was running a relatively simple job (read from Avro files, do a map, do another map, write back to Avro files) - 412 of 413 tasks completed, but the last task was hung in RUNNING state - The 412 successful tasks completed in median time 3.4s - The last hung task didn't finish even in 20 hours - The executor with the hung task was responsible for 100% of one core of CPU usage - Jstack of the executor attached (relevant thread pasted below) *Diagnosis* After doing some code spelunking, we determined the issue was concurrent use of a Configuration object for each task on an executor. In Hadoop each task runs in its own JVM, but in Spark multiple tasks can run in the same JVM, so the single-threaded access assumptions of the Configuration object no longer hold in Spark. The specific issue is that the AvroRecordReader actually _modifies_ the JobConf it's given when it's instantiated! It adds a key for the RPC protocol engine in the process of connecting to the Hadoop FileSystem. When many tasks start at the same time (like at the start of a job), many tasks are adding this configuration item to the one Configuration object at once. Internally Configuration uses a java.lang.HashMap, which isn't threadsafe… The below post is an excellent explanation of what happens in the situation where multiple threads insert into a HashMap at the same time. http://mailinator.blogspot.com/2009/06/beautiful-race-condition.html The gist is that you have a thread following a cycle of linked list nodes indefinitely. This exactly matches our observations of the 100% CPU core and also the final location in the stack trace. So it seems the way Spark shares a Configuration object between task threads in an executor is incorrect. We need some way to prevent concurrent access to a single Configuration object. *Proposed fix* We can clone the JobConf object in HadoopRDD.getJobConf() so each task gets its own JobConf object (and thus Configuration object). The optimization of broadcasting the Configuration object across the cluster can remain, but on the other side I think it needs to be cloned for each task to allow for concurrent access. I'm not sure the performance implications, but the comments suggest that the Configuration object is ~10KB so I would expect a clone on the object to be relatively speedy. Has this been observed before? Does my suggested fix make sense? I'd be happy to file a Jira ticket and continue discussion there for the right way to fix. Thanks! Andrew P.S. For others seeing this issue, our temporary workaround is to enable spark.speculation, which retries failed (or hung) tasks on other machines. Executor task launch worker-6 daemon prio=10 tid=0x7f91f01fe000 nid=0x54b1 runnable [0x7f92d74f1000] java.lang.Thread.State: RUNNABLE at java.util.HashMap.transfer(HashMap.java:601) at java.util.HashMap.resize(HashMap.java:581) at java.util.HashMap.addEntry(HashMap.java:879) at java.util.HashMap.put(HashMap.java:505) at org.apache.hadoop.conf.Configuration.set(Configuration.java:803) at org.apache.hadoop.conf.Configuration.set(Configuration.java:783) at org.apache.hadoop.conf.Configuration.setClass(Configuration.java:1662) at org.apache.hadoop.ipc.RPC.setProtocolEngine(RPC.java:193) at org.apache.hadoop.hdfs.NameNodeProxies.createNNProxyWithClientProtocol(NameNodeProxies.java:343) at org.apache.hadoop.hdfs.NameNodeProxies.createNonHAProxy(NameNodeProxies.java:168) at org.apache.hadoop.hdfs.NameNodeProxies.createProxy(NameNodeProxies.java:129) at org.apache.hadoop.hdfs.DFSClient.init(DFSClient.java:436) at org.apache.hadoop.hdfs.DFSClient.init(DFSClient.java:403) at org.apache.hadoop.hdfs.DistributedFileSystem.initialize(DistributedFileSystem.java:125) at org.apache.hadoop.fs.FileSystem.createFileSystem(FileSystem.java:2262) at org.apache.hadoop.fs.FileSystem.access$200(FileSystem.java:86) at org.apache.hadoop.fs.FileSystem$Cache.getInternal(FileSystem.java:2296) at org.apache.hadoop.fs.FileSystem$Cache.get(FileSystem.java:2278) at
Re: Hadoop's Configuration object isn't threadsafe
Hi Shengzhe, Even if we did make Configuration threadsafe, it'd take quite some time for that to trickle down to a Hadoop release that we could actually rely on Spark users having installed. I agree we should consider whether making Configuration threadsafe is something that Hadoop should do, but for the short term I think Spark needs to be able to handle the common scenario of Configuration being single-threaded. Thanks! Andrew On Tue, Jul 15, 2014 at 2:43 PM, yao yaosheng...@gmail.com wrote: Good catch Andrew. In addition to your proposed solution, is that possible to fix Configuration class and make it thread-safe ? I think the fix should be trivial, just use a ConcurrentHashMap, but I am not sure if we can push this change upstream (will hadoop guys accept this change ? for them, it seems they never expect Configuration object being accessed by multiple threads). -Shengzhe On Mon, Jul 14, 2014 at 10:22 PM, Andrew Ash and...@andrewash.com wrote: Hi Spark devs, We discovered a very interesting bug in Spark at work last week in Spark 0.9.1 — that the way Spark uses the Hadoop Configuration object is prone to thread safety issues. I believe it still applies in Spark 1.0.1 as well. Let me explain: *Observations* - Was running a relatively simple job (read from Avro files, do a map, do another map, write back to Avro files) - 412 of 413 tasks completed, but the last task was hung in RUNNING state - The 412 successful tasks completed in median time 3.4s - The last hung task didn't finish even in 20 hours - The executor with the hung task was responsible for 100% of one core of CPU usage - Jstack of the executor attached (relevant thread pasted below) *Diagnosis* After doing some code spelunking, we determined the issue was concurrent use of a Configuration object for each task on an executor. In Hadoop each task runs in its own JVM, but in Spark multiple tasks can run in the same JVM, so the single-threaded access assumptions of the Configuration object no longer hold in Spark. The specific issue is that the AvroRecordReader actually _modifies_ the JobConf it's given when it's instantiated! It adds a key for the RPC protocol engine in the process of connecting to the Hadoop FileSystem. When many tasks start at the same time (like at the start of a job), many tasks are adding this configuration item to the one Configuration object at once. Internally Configuration uses a java.lang.HashMap, which isn't threadsafe… The below post is an excellent explanation of what happens in the situation where multiple threads insert into a HashMap at the same time. http://mailinator.blogspot.com/2009/06/beautiful-race-condition.html The gist is that you have a thread following a cycle of linked list nodes indefinitely. This exactly matches our observations of the 100% CPU core and also the final location in the stack trace. So it seems the way Spark shares a Configuration object between task threads in an executor is incorrect. We need some way to prevent concurrent access to a single Configuration object. *Proposed fix* We can clone the JobConf object in HadoopRDD.getJobConf() so each task gets its own JobConf object (and thus Configuration object). The optimization of broadcasting the Configuration object across the cluster can remain, but on the other side I think it needs to be cloned for each task to allow for concurrent access. I'm not sure the performance implications, but the comments suggest that the Configuration object is ~10KB so I would expect a clone on the object to be relatively speedy. Has this been observed before? Does my suggested fix make sense? I'd be happy to file a Jira ticket and continue discussion there for the right way to fix. Thanks! Andrew P.S. For others seeing this issue, our temporary workaround is to enable spark.speculation, which retries failed (or hung) tasks on other machines. Executor task launch worker-6 daemon prio=10 tid=0x7f91f01fe000 nid=0x54b1 runnable [0x7f92d74f1000] java.lang.Thread.State: RUNNABLE at java.util.HashMap.transfer(HashMap.java:601) at java.util.HashMap.resize(HashMap.java:581) at java.util.HashMap.addEntry(HashMap.java:879) at java.util.HashMap.put(HashMap.java:505) at org.apache.hadoop.conf.Configuration.set(Configuration.java:803) at org.apache.hadoop.conf.Configuration.set(Configuration.java:783) at org.apache.hadoop.conf.Configuration.setClass(Configuration.java:1662) at org.apache.hadoop.ipc.RPC.setProtocolEngine(RPC.java:193) at org.apache.hadoop.hdfs.NameNodeProxies.createNNProxyWithClientProtocol(NameNodeProxies.java:343) at org.apache.hadoop.hdfs.NameNodeProxies.createNonHAProxy(NameNodeProxies.java:168) at
Re: Hadoop's Configuration object isn't threadsafe
Hey Andrew, Cloning the conf this might be a good/simple fix for this particular problem. It's definitely worth looking into. There are a few things we can probably do in Spark to deal with non-thread-safety inside of the Hadoop FileSystem and Configuration classes. One thing we can do in general is to add barriers around the locations where we knowingly access Hadoop FileSystem and Configuration state from multiple threads (e.g. during our own calls to getRecordReader in this case). But this will only deal with writer writer conflicts where we had multiple calls mutating the same object at the same time. It won't deal with reader writer conflicts where some of our initialization code touches state that is needed during normal execution of other tasks. - Patrick On Tue, Jul 15, 2014 at 12:56 PM, Andrew Ash and...@andrewash.com wrote: Hi Shengzhe, Even if we did make Configuration threadsafe, it'd take quite some time for that to trickle down to a Hadoop release that we could actually rely on Spark users having installed. I agree we should consider whether making Configuration threadsafe is something that Hadoop should do, but for the short term I think Spark needs to be able to handle the common scenario of Configuration being single-threaded. Thanks! Andrew On Tue, Jul 15, 2014 at 2:43 PM, yao yaosheng...@gmail.com wrote: Good catch Andrew. In addition to your proposed solution, is that possible to fix Configuration class and make it thread-safe ? I think the fix should be trivial, just use a ConcurrentHashMap, but I am not sure if we can push this change upstream (will hadoop guys accept this change ? for them, it seems they never expect Configuration object being accessed by multiple threads). -Shengzhe On Mon, Jul 14, 2014 at 10:22 PM, Andrew Ash and...@andrewash.com wrote: Hi Spark devs, We discovered a very interesting bug in Spark at work last week in Spark 0.9.1 -- that the way Spark uses the Hadoop Configuration object is prone to thread safety issues. I believe it still applies in Spark 1.0.1 as well. Let me explain: *Observations* - Was running a relatively simple job (read from Avro files, do a map, do another map, write back to Avro files) - 412 of 413 tasks completed, but the last task was hung in RUNNING state - The 412 successful tasks completed in median time 3.4s - The last hung task didn't finish even in 20 hours - The executor with the hung task was responsible for 100% of one core of CPU usage - Jstack of the executor attached (relevant thread pasted below) *Diagnosis* After doing some code spelunking, we determined the issue was concurrent use of a Configuration object for each task on an executor. In Hadoop each task runs in its own JVM, but in Spark multiple tasks can run in the same JVM, so the single-threaded access assumptions of the Configuration object no longer hold in Spark. The specific issue is that the AvroRecordReader actually _modifies_ the JobConf it's given when it's instantiated! It adds a key for the RPC protocol engine in the process of connecting to the Hadoop FileSystem. When many tasks start at the same time (like at the start of a job), many tasks are adding this configuration item to the one Configuration object at once. Internally Configuration uses a java.lang.HashMap, which isn't threadsafe... The below post is an excellent explanation of what happens in the situation where multiple threads insert into a HashMap at the same time. http://mailinator.blogspot.com/2009/06/beautiful-race-condition.html The gist is that you have a thread following a cycle of linked list nodes indefinitely. This exactly matches our observations of the 100% CPU core and also the final location in the stack trace. So it seems the way Spark shares a Configuration object between task threads in an executor is incorrect. We need some way to prevent concurrent access to a single Configuration object. *Proposed fix* We can clone the JobConf object in HadoopRDD.getJobConf() so each task gets its own JobConf object (and thus Configuration object). The optimization of broadcasting the Configuration object across the cluster can remain, but on the other side I think it needs to be cloned for each task to allow for concurrent access. I'm not sure the performance implications, but the comments suggest that the Configuration object is ~10KB so I would expect a clone on the object to be relatively speedy. Has this been observed before? Does my suggested fix make sense? I'd be happy to file a Jira ticket and continue discussion there for the right way to fix. Thanks! Andrew P.S. For others seeing this issue, our temporary workaround is to enable spark.speculation, which retries failed (or hung) tasks on other machines. Executor task launch worker-6 daemon prio=10
