Mixing
Spring batch Parrall step
I have spring-batch job, where i need to pass as an input to the job a list of id's, I would want that from that list of id's to be able pass to a step that could run all of them in parrallel. As for now what I've accomplish is run multiple job instance in a threadpoolExecutor, that executes the job x number of time. This implies that it does single queries for all jobs. And we are talking about over 50 millions records. The records represents a timeseries @specific day a consumption. I need for an id and batchId aggregate by month and send this information to a broker.
- Reader -> reads from the database according to an id and a timestamps
representing a time series. - Processor -> PassThroughItemProcessor
- Writer -> Send to AMQP (aggregates the the list of items)
Is there any best practice you could provide me ?
According to the suggestions, this is how my partitioner looks like ;
@Override
public Map<String, ExecutionContext> partition(int gridSize) {
log.debug("START: Partition");
Map<String, ExecutionContext> partitionMap = new HashMap<>();
final AtomicInteger counter = new AtomicInteger(0);
final AtomicInteger partitionerCounter = new AtomicInteger(0);
Page<Integer> result = null;
do {
result = repository.findDistinctByBatchId(LocalDateTime.parse(batchId, AipForecastService.DEFAULT_DATE_TIME_FORMATTER), Optional.ofNullable(result)
.map(Page::nextPageable)
.orElse(PageRequest.of(0, 100000)));
result
.stream()
.collect(Collectors.groupingBy(it -> counter.getAndIncrement() / 100))
.values()
.forEach(listOfInstallation -> {
ExecutionContext context = new ExecutionContext();
context.put("listOfInstallation", listOfInstallation);
partitionMap.put("partition" + partitionerCounter.incrementAndGet(), context);
log.debug("Adding to the partition map {}, listOfInstallation {}", partitionerCounter.get(), listOfInstallation);
});
} while (result.hasNext());
log.debug("END: Created Partitions for installation job of size:{}", partitionMap.size());
return partitionMap;
}
spring-batch
asked Nov 19 '18 at 14:47
Jimmy Bway
226
add a comment |
I have spring-batch job, where i need to pass as an input to the job a list of id's, I would want that from that list of id's to be able pass to a step that could run all of them in parrallel. As for now what I've accomplish is run multiple job instance in a threadpoolExecutor, that executes the job x number of time. This implies that it does single queries for all jobs. And we are talking about over 50 millions records. The records represents a timeseries @specific day a consumption. I need for an id and batchId aggregate by month and send this information to a broker.
- Reader -> reads from the database according to an id and a timestamps
representing a time series. - Processor -> PassThroughItemProcessor
- Writer -> Send to AMQP (aggregates the the list of items)
Is there any best practice you could provide me ?
According to the suggestions, this is how my partitioner looks like ;
@Override
public Map<String, ExecutionContext> partition(int gridSize) {
log.debug("START: Partition");
Map<String, ExecutionContext> partitionMap = new HashMap<>();
final AtomicInteger counter = new AtomicInteger(0);
final AtomicInteger partitionerCounter = new AtomicInteger(0);
Page<Integer> result = null;
do {
result = repository.findDistinctByBatchId(LocalDateTime.parse(batchId, AipForecastService.DEFAULT_DATE_TIME_FORMATTER), Optional.ofNullable(result)
.map(Page::nextPageable)
.orElse(PageRequest.of(0, 100000)));
result
.stream()
.collect(Collectors.groupingBy(it -> counter.getAndIncrement() / 100))
.values()
.forEach(listOfInstallation -> {
ExecutionContext context = new ExecutionContext();
context.put("listOfInstallation", listOfInstallation);
partitionMap.put("partition" + partitionerCounter.incrementAndGet(), context);
log.debug("Adding to the partition map {}, listOfInstallation {}", partitionerCounter.get(), listOfInstallation);
});
} while (result.hasNext());
log.debug("END: Created Partitions for installation job of size:{}", partitionMap.size());
return partitionMap;
}
spring-batch
asked Nov 19 '18 at 14:47
Jimmy Bway
226
add a comment |
I have spring-batch job, where i need to pass as an input to the job a list of id's, I would want that from that list of id's to be able pass to a step that could run all of them in parrallel. As for now what I've accomplish is run multiple job instance in a threadpoolExecutor, that executes the job x number of time. This implies that it does single queries for all jobs. And we are talking about over 50 millions records. The records represents a timeseries @specific day a consumption. I need for an id and batchId aggregate by month and send this information to a broker.
- Reader -> reads from the database according to an id and a timestamps
representing a time series. - Processor -> PassThroughItemProcessor
- Writer -> Send to AMQP (aggregates the the list of items)
Is there any best practice you could provide me ?
According to the suggestions, this is how my partitioner looks like ;
@Override
public Map<String, ExecutionContext> partition(int gridSize) {
log.debug("START: Partition");
Map<String, ExecutionContext> partitionMap = new HashMap<>();
final AtomicInteger counter = new AtomicInteger(0);
final AtomicInteger partitionerCounter = new AtomicInteger(0);
Page<Integer> result = null;
do {
result = repository.findDistinctByBatchId(LocalDateTime.parse(batchId, AipForecastService.DEFAULT_DATE_TIME_FORMATTER), Optional.ofNullable(result)
.map(Page::nextPageable)
.orElse(PageRequest.of(0, 100000)));
result
.stream()
.collect(Collectors.groupingBy(it -> counter.getAndIncrement() / 100))
.values()
.forEach(listOfInstallation -> {
ExecutionContext context = new ExecutionContext();
context.put("listOfInstallation", listOfInstallation);
partitionMap.put("partition" + partitionerCounter.incrementAndGet(), context);
log.debug("Adding to the partition map {}, listOfInstallation {}", partitionerCounter.get(), listOfInstallation);
});
} while (result.hasNext());
log.debug("END: Created Partitions for installation job of size:{}", partitionMap.size());
return partitionMap;
}
spring-batch
asked Nov 19 '18 at 14:47
Jimmy Bway
226
I have spring-batch job, where i need to pass as an input to the job a list of id's, I would want that from that list of id's to be able pass to a step that could run all of them in parrallel. As for now what I've accomplish is run multiple job instance in a threadpoolExecutor, that executes the job x number of time. This implies that it does single queries for all jobs. And we are talking about over 50 millions records. The records represents a timeseries @specific day a consumption. I need for an id and batchId aggregate by month and send this information to a broker.
- Reader -> reads from the database according to an id and a timestamps
representing a time series. - Processor -> PassThroughItemProcessor
- Writer -> Send to AMQP (aggregates the the list of items)
Is there any best practice you could provide me ?
According to the suggestions, this is how my partitioner looks like ;
@Override
public Map<String, ExecutionContext> partition(int gridSize) {
log.debug("START: Partition");
Map<String, ExecutionContext> partitionMap = new HashMap<>();
final AtomicInteger counter = new AtomicInteger(0);
final AtomicInteger partitionerCounter = new AtomicInteger(0);
Page<Integer> result = null;
do {
result = repository.findDistinctByBatchId(LocalDateTime.parse(batchId, AipForecastService.DEFAULT_DATE_TIME_FORMATTER), Optional.ofNullable(result)
.map(Page::nextPageable)
.orElse(PageRequest.of(0, 100000)));
result
.stream()
.collect(Collectors.groupingBy(it -> counter.getAndIncrement() / 100))
.values()
.forEach(listOfInstallation -> {
ExecutionContext context = new ExecutionContext();
context.put("listOfInstallation", listOfInstallation);
partitionMap.put("partition" + partitionerCounter.incrementAndGet(), context);
log.debug("Adding to the partition map {}, listOfInstallation {}", partitionerCounter.get(), listOfInstallation);
});
} while (result.hasNext());
log.debug("END: Created Partitions for installation job of size:{}", partitionMap.size());
return partitionMap;
}
spring-batch
spring-batch
asked Nov 19 '18 at 14:47
Jimmy Bway
226
asked Nov 19 '18 at 14:47
Jimmy Bway
226
edited Nov 23 '18 at 13:31
asked Nov 19 '18 at 14:47
Jimmy Bway
226
asked Nov 19 '18 at 14:47
Jimmy Bway
226
asked Nov 19 '18 at 14:47
Jimmy Bway
226
226
add a comment |
add a comment |
1 Answer
1
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oldest
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i need to pass as an input to the job a list of id's, I would want that from that list of id's to be able pass to a step that could run all of them in parrallel
You can partition that list and use a partitioned step to process partitions in parallel.
Is there any best practice you could provide me ?
If you choose the partitioned step route (which looks appropriate to me for your use case), I would recommend to not create a partition per id (unless you have a reasonable number of IDs). You can create for example a partition per range of IDs and make each worker step do the read/process/write logic you described which could be definitely done in parallel.
Hope this helps.
answered Nov 20 '18 at 8:36
Mahmoud Ben Hassine
3,8501714
Thanks for the reply, when you Say "i would recommend to notre create a partition per ID unless you have reasonable number of IDs" does 240k sounds reasonable for you ? 240k represents the total number of ID from wich a timeseries is associate. Possible results could be 240k * 365 Days * 2 years = 262800000. Also i was wondering if you could provide a concrete example of how to use the partionner for both case you mentionned
– Jimmy Bway
Nov 20 '18 at 12:25
Also need to keep in mind that my Writer have to send the aggregated data for a specific by YearMonth, therefore the payload sent to the Exchange has to represent a full YearMonth
– Jimmy Bway
Nov 20 '18 at 12:36
1
240k partitions is too high. So having a partition for each ID is not a good option for you. You can partition by range in that case. There is no best value for the number of partitions, but 100 is a good start I think. For examples, you can find one for local partitioning here: github.com/spring-projects/spring-batch/tree/master/… and another one for remote partitioning here: github.com/spring-projects/spring-batch/tree/master/…
– Mahmoud Ben Hassine
Nov 20 '18 at 14:26
Hi, I've tried as you pointed out. Perhaps using the Default Implementation of the PartitionHandler, makes futureCalls and blocking with (.get()) until the query is all executors returned the rs. Isn't there a better way I could implement ?
– Jimmy Bway
Nov 22 '18 at 21:17
I've paste the content of my implementation of the partitioner.
– Jimmy Bway
Nov 23 '18 at 13:22
add a comment |
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i need to pass as an input to the job a list of id's, I would want that from that list of id's to be able pass to a step that could run all of them in parrallel
You can partition that list and use a partitioned step to process partitions in parallel.
Is there any best practice you could provide me ?
If you choose the partitioned step route (which looks appropriate to me for your use case), I would recommend to not create a partition per id (unless you have a reasonable number of IDs). You can create for example a partition per range of IDs and make each worker step do the read/process/write logic you described which could be definitely done in parallel.
Hope this helps.
answered Nov 20 '18 at 8:36
Mahmoud Ben Hassine
3,8501714
Thanks for the reply, when you Say "i would recommend to notre create a partition per ID unless you have reasonable number of IDs" does 240k sounds reasonable for you ? 240k represents the total number of ID from wich a timeseries is associate. Possible results could be 240k * 365 Days * 2 years = 262800000. Also i was wondering if you could provide a concrete example of how to use the partionner for both case you mentionned
– Jimmy Bway
Nov 20 '18 at 12:25
Also need to keep in mind that my Writer have to send the aggregated data for a specific by YearMonth, therefore the payload sent to the Exchange has to represent a full YearMonth
– Jimmy Bway
Nov 20 '18 at 12:36
1
240k partitions is too high. So having a partition for each ID is not a good option for you. You can partition by range in that case. There is no best value for the number of partitions, but 100 is a good start I think. For examples, you can find one for local partitioning here: github.com/spring-projects/spring-batch/tree/master/… and another one for remote partitioning here: github.com/spring-projects/spring-batch/tree/master/…
– Mahmoud Ben Hassine
Nov 20 '18 at 14:26
Hi, I've tried as you pointed out. Perhaps using the Default Implementation of the PartitionHandler, makes futureCalls and blocking with (.get()) until the query is all executors returned the rs. Isn't there a better way I could implement ?
– Jimmy Bway
Nov 22 '18 at 21:17
I've paste the content of my implementation of the partitioner.
– Jimmy Bway
Nov 23 '18 at 13:22
add a comment |
i need to pass as an input to the job a list of id's, I would want that from that list of id's to be able pass to a step that could run all of them in parrallel
You can partition that list and use a partitioned step to process partitions in parallel.
Is there any best practice you could provide me ?
If you choose the partitioned step route (which looks appropriate to me for your use case), I would recommend to not create a partition per id (unless you have a reasonable number of IDs). You can create for example a partition per range of IDs and make each worker step do the read/process/write logic you described which could be definitely done in parallel.
Hope this helps.
answered Nov 20 '18 at 8:36
Mahmoud Ben Hassine
3,8501714
Thanks for the reply, when you Say "i would recommend to notre create a partition per ID unless you have reasonable number of IDs" does 240k sounds reasonable for you ? 240k represents the total number of ID from wich a timeseries is associate. Possible results could be 240k * 365 Days * 2 years = 262800000. Also i was wondering if you could provide a concrete example of how to use the partionner for both case you mentionned
– Jimmy Bway
Nov 20 '18 at 12:25
Also need to keep in mind that my Writer have to send the aggregated data for a specific by YearMonth, therefore the payload sent to the Exchange has to represent a full YearMonth
– Jimmy Bway
Nov 20 '18 at 12:36
1
240k partitions is too high. So having a partition for each ID is not a good option for you. You can partition by range in that case. There is no best value for the number of partitions, but 100 is a good start I think. For examples, you can find one for local partitioning here: github.com/spring-projects/spring-batch/tree/master/… and another one for remote partitioning here: github.com/spring-projects/spring-batch/tree/master/…
– Mahmoud Ben Hassine
Nov 20 '18 at 14:26
Hi, I've tried as you pointed out. Perhaps using the Default Implementation of the PartitionHandler, makes futureCalls and blocking with (.get()) until the query is all executors returned the rs. Isn't there a better way I could implement ?
– Jimmy Bway
Nov 22 '18 at 21:17
I've paste the content of my implementation of the partitioner.
– Jimmy Bway
Nov 23 '18 at 13:22
add a comment |
i need to pass as an input to the job a list of id's, I would want that from that list of id's to be able pass to a step that could run all of them in parrallel
You can partition that list and use a partitioned step to process partitions in parallel.
Is there any best practice you could provide me ?
If you choose the partitioned step route (which looks appropriate to me for your use case), I would recommend to not create a partition per id (unless you have a reasonable number of IDs). You can create for example a partition per range of IDs and make each worker step do the read/process/write logic you described which could be definitely done in parallel.
Hope this helps.
answered Nov 20 '18 at 8:36
Mahmoud Ben Hassine
3,8501714
i need to pass as an input to the job a list of id's, I would want that from that list of id's to be able pass to a step that could run all of them in parrallel
You can partition that list and use a partitioned step to process partitions in parallel.
Is there any best practice you could provide me ?
If you choose the partitioned step route (which looks appropriate to me for your use case), I would recommend to not create a partition per id (unless you have a reasonable number of IDs). You can create for example a partition per range of IDs and make each worker step do the read/process/write logic you described which could be definitely done in parallel.
Hope this helps.
answered Nov 20 '18 at 8:36
Mahmoud Ben Hassine
3,8501714
answered Nov 20 '18 at 8:36
Mahmoud Ben Hassine
3,8501714
answered Nov 20 '18 at 8:36
Mahmoud Ben Hassine
3,8501714
answered Nov 20 '18 at 8:36
Mahmoud Ben Hassine
3,8501714
3,8501714
Thanks for the reply, when you Say "i would recommend to notre create a partition per ID unless you have reasonable number of IDs" does 240k sounds reasonable for you ? 240k represents the total number of ID from wich a timeseries is associate. Possible results could be 240k * 365 Days * 2 years = 262800000. Also i was wondering if you could provide a concrete example of how to use the partionner for both case you mentionned
– Jimmy Bway
Nov 20 '18 at 12:25
Also need to keep in mind that my Writer have to send the aggregated data for a specific by YearMonth, therefore the payload sent to the Exchange has to represent a full YearMonth
– Jimmy Bway
Nov 20 '18 at 12:36
1
240k partitions is too high. So having a partition for each ID is not a good option for you. You can partition by range in that case. There is no best value for the number of partitions, but 100 is a good start I think. For examples, you can find one for local partitioning here: github.com/spring-projects/spring-batch/tree/master/… and another one for remote partitioning here: github.com/spring-projects/spring-batch/tree/master/…
– Mahmoud Ben Hassine
Nov 20 '18 at 14:26
Hi, I've tried as you pointed out. Perhaps using the Default Implementation of the PartitionHandler, makes futureCalls and blocking with (.get()) until the query is all executors returned the rs. Isn't there a better way I could implement ?
– Jimmy Bway
Nov 22 '18 at 21:17
I've paste the content of my implementation of the partitioner.
– Jimmy Bway
Nov 23 '18 at 13:22
add a comment |
Thanks for the reply, when you Say "i would recommend to notre create a partition per ID unless you have reasonable number of IDs" does 240k sounds reasonable for you ? 240k represents the total number of ID from wich a timeseries is associate. Possible results could be 240k * 365 Days * 2 years = 262800000. Also i was wondering if you could provide a concrete example of how to use the partionner for both case you mentionned
– Jimmy Bway
Nov 20 '18 at 12:25
Also need to keep in mind that my Writer have to send the aggregated data for a specific by YearMonth, therefore the payload sent to the Exchange has to represent a full YearMonth
– Jimmy Bway
Nov 20 '18 at 12:36
1
240k partitions is too high. So having a partition for each ID is not a good option for you. You can partition by range in that case. There is no best value for the number of partitions, but 100 is a good start I think. For examples, you can find one for local partitioning here: github.com/spring-projects/spring-batch/tree/master/… and another one for remote partitioning here: github.com/spring-projects/spring-batch/tree/master/…
– Mahmoud Ben Hassine
Nov 20 '18 at 14:26
Hi, I've tried as you pointed out. Perhaps using the Default Implementation of the PartitionHandler, makes futureCalls and blocking with (.get()) until the query is all executors returned the rs. Isn't there a better way I could implement ?
– Jimmy Bway
Nov 22 '18 at 21:17
I've paste the content of my implementation of the partitioner.
– Jimmy Bway
Nov 23 '18 at 13:22
Thanks for the reply, when you Say "i would recommend to notre create a partition per ID unless you have reasonable number of IDs" does 240k sounds reasonable for you ? 240k represents the total number of ID from wich a timeseries is associate. Possible results could be 240k * 365 Days * 2 years = 262800000. Also i was wondering if you could provide a concrete example of how to use the partionner for both case you mentionned
– Jimmy Bway
Nov 20 '18 at 12:25
Thanks for the reply, when you Say "i would recommend to notre create a partition per ID unless you have reasonable number of IDs" does 240k sounds reasonable for you ? 240k represents the total number of ID from wich a timeseries is associate. Possible results could be 240k * 365 Days * 2 years = 262800000. Also i was wondering if you could provide a concrete example of how to use the partionner for both case you mentionned
– Jimmy Bway
Nov 20 '18 at 12:25
Also need to keep in mind that my Writer have to send the aggregated data for a specific by YearMonth, therefore the payload sent to the Exchange has to represent a full YearMonth
– Jimmy Bway
Nov 20 '18 at 12:36
Also need to keep in mind that my Writer have to send the aggregated data for a specific by YearMonth, therefore the payload sent to the Exchange has to represent a full YearMonth
– Jimmy Bway
Nov 20 '18 at 12:36
1
1
240k partitions is too high. So having a partition for each ID is not a good option for you. You can partition by range in that case. There is no best value for the number of partitions, but 100 is a good start I think. For examples, you can find one for local partitioning here: github.com/spring-projects/spring-batch/tree/master/… and another one for remote partitioning here: github.com/spring-projects/spring-batch/tree/master/…
– Mahmoud Ben Hassine
Nov 20 '18 at 14:26
240k partitions is too high. So having a partition for each ID is not a good option for you. You can partition by range in that case. There is no best value for the number of partitions, but 100 is a good start I think. For examples, you can find one for local partitioning here: github.com/spring-projects/spring-batch/tree/master/… and another one for remote partitioning here: github.com/spring-projects/spring-batch/tree/master/…
– Mahmoud Ben Hassine
Nov 20 '18 at 14:26
Hi, I've tried as you pointed out. Perhaps using the Default Implementation of the PartitionHandler, makes futureCalls and blocking with (.get()) until the query is all executors returned the rs. Isn't there a better way I could implement ?
– Jimmy Bway
Nov 22 '18 at 21:17
Hi, I've tried as you pointed out. Perhaps using the Default Implementation of the PartitionHandler, makes futureCalls and blocking with (.get()) until the query is all executors returned the rs. Isn't there a better way I could implement ?
– Jimmy Bway
Nov 22 '18 at 21:17
I've paste the content of my implementation of the partitioner.
– Jimmy Bway
Nov 23 '18 at 13:22
I've paste the content of my implementation of the partitioner.
– Jimmy Bway
Nov 23 '18 at 13:22
add a comment |
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