executor | file changes and then execute command | Command Line Interface library

The actual value being passed as the second argument games to getRecentWinners is listOfGames, which as a values of [1, 2, 3 ... 21]. But the first line of getRecentWinners is:

First make a Repository class and make an instance of your DAO

I think I've found the problem. It happens that when I'm executing the "removeValue()" on firebase, it triggers it asynchronously. So, the for Loop is finished quite quickly giving me the LogCat above. I have added an OnSuccessListener to the remove command and am increasing the progress in there. Also, I've added a control variable in order to know when the processing is finished and thus close the progressbar. The end code, is like that:

One of the contributors to Debezium seems to affirm this is a product bug https://gitter.im/debezium/user?at=60b8e96778e1d6477d7f40b5. I have created an issue https://issues.redhat.com/browse/DBZ-3597.

Edit:

A PR was published and approved to fix the issue. The fix is in the current 1.6 beta snapshot build.

There is a possible workaround. The names of indices are the key to the problem. It seems they are processed in alphabetical order. Only the first one is taken into consideration so if you can rename your indices to have the one with keys first then you should get unblocked.

Executers are the entities that perform the actual work. Each executor is its own JVM process with allotted memory.

(As described here: http://spark.apache.org/docs/latest/cluster-overview.html)

The broadcast is materialized at the executer level so in the above example- 10GB.

Support for _PIP_ADDITIONAL_REQUIREMENTS environment variable has not been released yet. It is only supported by the developer/unreleased version of the docker image. It is planned that this feature will be available in Airflow 2.1.1. For more information, see: Adding extra requirements for build and runtime of the PROD image.

For the older version, you should build a new image and set this image in the docker-compose.yaml. To do this, you need to follow a few steps.

1. Create a new Dockerfile with the following content:

I managed to solve this issue by using this solution:

gcloud-command-not-found-while-installing-google-cloud-sdk

I included this 2 line into my gitlab-ci.yml before using the gsutil command.

At every iteration, you re-open your file using a FileWriter. By default, it starts writing at the beginning of the file, thus overwriting its contents with always the same "Hello World" string.

If you want to add that sentence to the end, then you want to set the "append" option when instanciating your FileWriter. Also append a line separator each time:

I answer as I know things on each part, possibly disregarding a few of your assertions:

I have four questions. Suppose in spark I have 3 worker nodes. Each worker node has 3 executors and each executor has 3 cores. Each executor has 5 gb memory. (Total 6 executors, 27 cores and 15gb memory). What will happen if: >>> I would use 1 Executor, 1 Core. That is the generally accepted paradigm afaik.

• I have 30 data partitions. Each partition is of size 6 gb. Optimally, the number of partitions must be equal to number of cores, since each core executes one partition/task (One task per partition). Now in this case, how will each executor-core will process the partition since partition size is greater than the available executor memory? Note: I'm not calling cache() or persist(), it's simply that I'm applying some narrow transformations like map() and filter() on my rdd. >>> The number of partitions being the same of number of cores is not true. You can service 1000 partitions with 10 cores, processing one at a time. What if you have 100K partition and on-prem? Unlikely you will get 100K Executors. >>> Moving on and leaving Driver-side collect issues to one side: You may not have enough memory for a given operation on an Executor; Spark can spill to files to disk at the expense of speed of processing. However, the partition size should not exceed a maximum size, was beefed up some time ago. Using multi-core Executors failure can occur, i.e. OOM's, also a result of GC-issues, a difficult topic.

• Will spark automatically try to store the partitions on disk? (I'm not calling cache() or persist() but merely just transformations are happening after an action is called) >>> Not if it can avoid it, but when memory is tight, eviction / spilling to disk can and will occur, and in some cases re-computation from source or last checkpoint will occur.

• Since I have partitions (30) greater than the number of available cores (27) so at max, my cluster can process 27 partitions, what will happen to the remaining 3 partitions? Will they wait for the occupied cores to get freed? >>> They will be serviced by a free Executor at a point in time.

• If I'm calling persist() whose storage level is set to MEMORY_AND_DISK, then if partition size is greater than memory, it will spill data to the disk? On which disk this data will be stored? The worker node's external HDD? >>> Yes, and it will be spilled to the local file system. I think you can configure for HDFS via a setting, but local disks are faster.

This an insightful blog: https://medium.com/swlh/spark-oom-error-closeup-462c7a01709d