executors | C++ library for executors | Reactive Programming library
kandi X-RAY | executors Summary
kandi X-RAY | executors Summary
The central concept of this library is the executor. An executor embodies a set of rules about where, when and how to run a function object. For example:. Type of executor | Where, when and how ---------------- | ------------------- System | Any thread in the process. Thread pool | Any thread in the pool, and nowhere else. Strand | Not concurrent with any other function object sharing the strand, and in FIFO order. Future / Promise | Any thread. Capture any exceptions thrown by the function object and store them in the promise. Executors are ultimately defined by a set of type requirements, so the set of executors isn’t limited to those listed here. Like allocators, library users can develop custom executor types to implement their own rules. To submit a function object to an executor, we can choose from one of three fundamental operations: dispatch, post and defer. These operations differ in the eagerness with which they run the submitted function.
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executors Key Features
executors Examples and Code Snippets
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import java.util.concurrent.Callable;
class CallableTask implements Callable {
private String name;
public CallableTask(String name) {
this.name = name;
def sync_executors(self):
"""Sync both local executors and the ones on remote workers.
In async execution mode, local function calls can return before the
corresponding remote op/function execution requests are completed. Calling
thi
def clear_executor_errors(self):
"""Clear errors in both local executors and remote workers.
After receiving errors from remote workers, additional requests on the fly
could further taint the status on the remote workers due to the async
Community Discussions
Trending Discussions on executors
QUESTION
So I am relatively new to programming, and I have been working on this task app, where I want to save the data such as task name and more, given by the user. I am trying to accomplish this using Room. Now, initially, when I tried to do it, the app would crash since I was doing everything on the main thread probably. So, after a little research, I came to AsyncTask, but that is outdated. Now finally I have come across the Executer. I created a class for it, but I am a little unsure as to how I can implement it in my app. This is what I did :
Entity Class :
...ANSWER
Answered 2021-Jun-14 at 12:03First make a Repository class and make an instance of your DAO
QUESTION
I'm trying to implement an executorService to delete a huge Firebase node in background. This node gets a new record every ten seconds to feed a realtime linear graphic (259Krecords/month). The users need a function to clean the data from time to timen and they need to trigger it manually on demand.
I've coded the method below:
...ANSWER
Answered 2021-Jun-14 at 19:54I 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:
QUESTION
I am using the SQL connector to capture CDC on a table that we only expose a subset of all columns on the table. The table has two unique indexes A & B on it. Neither index is marked as the PRIMARY INDEX but index A is logically the primary key in our product and what I want to use with the connector. Index B references a column we don't expose to CDC. Index B isn't truly used in our product as a unique key for the table and it is only marked UNIQUE as it is known to be unique and marking it gives us a performance benefit.
This seems to be resulting in the error below. I've tried using the message.key.columns
option on the connector to specify index A as the key for this table and hopefully ignore index B. However, the connector seems to still want to do something with index B
- How can I work around this situation?
- For my own understanding, why does the connector care about indexes that reference columns not exposed by CDC?
- For my own understanding, why does the connector care about any index besides what is configured on the CDC table i.e. see CDC.change_tables.index_name documentation
ANSWER
Answered 2021-Jun-14 at 17:35One 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.
QUESTION
Assuming I have a cluster with two worker nodes and from these two workers, I have 10 executors. How much memory will be used up in my cluster if I choose to broadcast a 1gb Map?
Will it be 1gb per worker so 2gb in total? Or will it be 1gb per executor so 10gb in total?
Apologies for the simple question, but for me, a number of articles written about broadcast variables aren’t 100% clear on this issue.
...ANSWER
Answered 2021-Jun-14 at 17:17Executers 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.
QUESTION
I am using the following docker-compose image, I got this image from: https://github.com/apache/airflow/blob/main/docs/apache-airflow/start/docker-compose.yaml
...ANSWER
Answered 2021-Jun-14 at 16:35Support 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.
- Create a new
Dockerfile
with the following content:
QUESTION
I'm trying to write a line to a file every 5 seconds continuously. So let us say I have a String = Hello world
and I run my code for 15 seconds my output should be a file containing the data
ANSWER
Answered 2021-Jun-14 at 13:35At 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:
QUESTION
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 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.
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)
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?
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?
ANSWER
Answered 2021-Jun-14 at 13:26I 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
QUESTION
I am new to Spark and am trying to run on a hadoop cluster a simple spark jar file built through maven in intellij. But I am getting classnotfoundexception in all the ways I tried to submit the application through spark-submit.
My pom.xml:
...ANSWER
Answered 2021-Jun-14 at 09:36You need to add scala-compiler configuration to your pom.xml
. The problem is without that there is nothing to compile your SparkTrans.scala file into java classes.
Add:
QUESTION
dispatcher-servlet.xml
...ANSWER
Answered 2021-Jun-14 at 02:53This issue is solved after correcting up my code
QUESTION
In CameraX Analysis, setTargetResolution(new Size(2560, 800), but in Analyzer imageProxy.getImage.getWidth=1280 and getHeight=400, and YUVToByte(imageProxy.getImage).length()=768000。In Camera, parameter.setPreviewSize(2560, 800) then byte[].length in onPreviewFrame is 3072000(equales 768000*(2560/1280)*(800/400))。How can I make CameraX Analyzer imageProxy.getImage.getWidth and getHeight = 2560 and 800, and YUVToByte(ImageProxy.getImage).length()=3072000? In CameraX onPreviewFrame(), res always = null, in Camera onPreviewFrame(), res can get currect value, what's the different between CameraX and Camera? And what should I do in CameraX?
CameraX:
...ANSWER
Answered 2021-Jun-13 at 01:15With regards to the image analysis resolution, the documentation of ImageAnalysis.Builder.setTargetResolution()
states that:
The maximum available resolution that could be selected for an ImageAnalysis is limited to be under 1080p.
So setting a size of 2560x800 won't work as you expect. In return CameraX seems to be selecting the maximum ImageAnalysis
resolution that has the same aspect ratio you requested (2560/800 = 1280/400).
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