Spark Structured Streaming and Spark-Ml Regression
Is it possible to apply Spark-Ml regression to streaming sources? I see there is StreamingLogisticRegressionWithSGD but It's for older RDD API and I couldn't use It with structured streaming sources.
- How I'm supposed to apply regressions on structured streaming sources?
- (A little OT) If I cannot use streaming API for regression how can I commit offsets or so to source in a batch processing way? (Kafka sink)
apache-spark apache-spark-sql apache-spark-ml
asked Jan 14 '18 at 11:17
ReithReith
1,563819
add a comment |
Is it possible to apply Spark-Ml regression to streaming sources? I see there is StreamingLogisticRegressionWithSGD but It's for older RDD API and I couldn't use It with structured streaming sources.
- How I'm supposed to apply regressions on structured streaming sources?
- (A little OT) If I cannot use streaming API for regression how can I commit offsets or so to source in a batch processing way? (Kafka sink)
apache-spark apache-spark-sql apache-spark-ml
asked Jan 14 '18 at 11:17
ReithReith
1,563819
add a comment |
Is it possible to apply Spark-Ml regression to streaming sources? I see there is StreamingLogisticRegressionWithSGD but It's for older RDD API and I couldn't use It with structured streaming sources.
- How I'm supposed to apply regressions on structured streaming sources?
- (A little OT) If I cannot use streaming API for regression how can I commit offsets or so to source in a batch processing way? (Kafka sink)
apache-spark apache-spark-sql apache-spark-ml
asked Jan 14 '18 at 11:17
ReithReith
1,563819
Is it possible to apply Spark-Ml regression to streaming sources? I see there is StreamingLogisticRegressionWithSGD but It's for older RDD API and I couldn't use It with structured streaming sources.
- How I'm supposed to apply regressions on structured streaming sources?
- (A little OT) If I cannot use streaming API for regression how can I commit offsets or so to source in a batch processing way? (Kafka sink)
apache-spark apache-spark-sql apache-spark-ml
apache-spark apache-spark-sql apache-spark-ml
asked Jan 14 '18 at 11:17
ReithReith
1,563819
asked Jan 14 '18 at 11:17
ReithReith
1,563819
asked Jan 14 '18 at 11:17
ReithReith
1,563819
asked Jan 14 '18 at 11:17
ReithReith
1,563819
asked Jan 14 '18 at 11:17
ReithReith
1,563819
1,563819
add a comment |
add a comment |
1 Answer
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Today (Spark 2.2 / 2.3) there is no support for machine learning in Structured Streaming and there is no ongoing work in this direction. Please follow SPARK-16424 to track future progress.
You can however:
Train iterative, non-distributed models using forEach sink and some form of external state storage. At a high level regression model could be implemented like this:
- Fetch latest model when calling
ForeachWriter.openand initialize loss accumulator (not in a Spark sense, just local variable) for the partition. - Compute loss for each record in
ForeachWriter.processand update accumulator. - Push loses to external store when calling
ForeachWriter.close. - This would leave external storage in charge with computing gradient and updating model with implementation dependent on the store.
- Fetch latest model when calling
Try to hack SQL queries (see https://github.com/holdenk/spark-structured-streaming-ml by Holden Karau)
Thanks. Can you elaborate more about forEach method? As I understand It's a method to do some action (like writing to db) on eachRow. But for applying regression I need to do some action (training and evaluating) on a dataset.
– Reith
Jan 14 '18 at 11:41
Thanks. I think I'll give up with streaming API since by external storage which computes gradients I feel I won't get any help from spark-ml.
– Reith
Jan 14 '18 at 11:50
True, although it might sound more serious than it is. After all with GD it is just a one line with any decent linear algebra lib. But if you're looking for built-in support in Structured Streaming there is none at the moment.
– hi-zir
Jan 14 '18 at 11:57
add a comment |
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1 Answer
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1 Answer
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active
oldest
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votes
Today (Spark 2.2 / 2.3) there is no support for machine learning in Structured Streaming and there is no ongoing work in this direction. Please follow SPARK-16424 to track future progress.
You can however:
Train iterative, non-distributed models using forEach sink and some form of external state storage. At a high level regression model could be implemented like this:
- Fetch latest model when calling
ForeachWriter.openand initialize loss accumulator (not in a Spark sense, just local variable) for the partition. - Compute loss for each record in
ForeachWriter.processand update accumulator. - Push loses to external store when calling
ForeachWriter.close. - This would leave external storage in charge with computing gradient and updating model with implementation dependent on the store.
- Fetch latest model when calling
Try to hack SQL queries (see https://github.com/holdenk/spark-structured-streaming-ml by Holden Karau)
Thanks. Can you elaborate more about forEach method? As I understand It's a method to do some action (like writing to db) on eachRow. But for applying regression I need to do some action (training and evaluating) on a dataset.
– Reith
Jan 14 '18 at 11:41
Thanks. I think I'll give up with streaming API since by external storage which computes gradients I feel I won't get any help from spark-ml.
– Reith
Jan 14 '18 at 11:50
True, although it might sound more serious than it is. After all with GD it is just a one line with any decent linear algebra lib. But if you're looking for built-in support in Structured Streaming there is none at the moment.
– hi-zir
Jan 14 '18 at 11:57
add a comment |
Today (Spark 2.2 / 2.3) there is no support for machine learning in Structured Streaming and there is no ongoing work in this direction. Please follow SPARK-16424 to track future progress.
You can however:
Train iterative, non-distributed models using forEach sink and some form of external state storage. At a high level regression model could be implemented like this:
- Fetch latest model when calling
ForeachWriter.openand initialize loss accumulator (not in a Spark sense, just local variable) for the partition. - Compute loss for each record in
ForeachWriter.processand update accumulator. - Push loses to external store when calling
ForeachWriter.close. - This would leave external storage in charge with computing gradient and updating model with implementation dependent on the store.
- Fetch latest model when calling
Try to hack SQL queries (see https://github.com/holdenk/spark-structured-streaming-ml by Holden Karau)
Thanks. Can you elaborate more about forEach method? As I understand It's a method to do some action (like writing to db) on eachRow. But for applying regression I need to do some action (training and evaluating) on a dataset.
– Reith
Jan 14 '18 at 11:41
Thanks. I think I'll give up with streaming API since by external storage which computes gradients I feel I won't get any help from spark-ml.
– Reith
Jan 14 '18 at 11:50
True, although it might sound more serious than it is. After all with GD it is just a one line with any decent linear algebra lib. But if you're looking for built-in support in Structured Streaming there is none at the moment.
– hi-zir
Jan 14 '18 at 11:57
add a comment |
Today (Spark 2.2 / 2.3) there is no support for machine learning in Structured Streaming and there is no ongoing work in this direction. Please follow SPARK-16424 to track future progress.
You can however:
Train iterative, non-distributed models using forEach sink and some form of external state storage. At a high level regression model could be implemented like this:
- Fetch latest model when calling
ForeachWriter.openand initialize loss accumulator (not in a Spark sense, just local variable) for the partition. - Compute loss for each record in
ForeachWriter.processand update accumulator. - Push loses to external store when calling
ForeachWriter.close. - This would leave external storage in charge with computing gradient and updating model with implementation dependent on the store.
- Fetch latest model when calling
Try to hack SQL queries (see https://github.com/holdenk/spark-structured-streaming-ml by Holden Karau)
Today (Spark 2.2 / 2.3) there is no support for machine learning in Structured Streaming and there is no ongoing work in this direction. Please follow SPARK-16424 to track future progress.
You can however:
Train iterative, non-distributed models using forEach sink and some form of external state storage. At a high level regression model could be implemented like this:
- Fetch latest model when calling
ForeachWriter.openand initialize loss accumulator (not in a Spark sense, just local variable) for the partition. - Compute loss for each record in
ForeachWriter.processand update accumulator. - Push loses to external store when calling
ForeachWriter.close. - This would leave external storage in charge with computing gradient and updating model with implementation dependent on the store.
- Fetch latest model when calling
Try to hack SQL queries (see https://github.com/holdenk/spark-structured-streaming-ml by Holden Karau)
edited Jan 14 '18 at 11:43
community wiki
3 revs
user8371915
Thanks. Can you elaborate more about forEach method? As I understand It's a method to do some action (like writing to db) on eachRow. But for applying regression I need to do some action (training and evaluating) on a dataset.
– Reith
Jan 14 '18 at 11:41
Thanks. I think I'll give up with streaming API since by external storage which computes gradients I feel I won't get any help from spark-ml.
– Reith
Jan 14 '18 at 11:50
True, although it might sound more serious than it is. After all with GD it is just a one line with any decent linear algebra lib. But if you're looking for built-in support in Structured Streaming there is none at the moment.
– hi-zir
Jan 14 '18 at 11:57
add a comment |
Thanks. Can you elaborate more about forEach method? As I understand It's a method to do some action (like writing to db) on eachRow. But for applying regression I need to do some action (training and evaluating) on a dataset.
– Reith
Jan 14 '18 at 11:41
Thanks. I think I'll give up with streaming API since by external storage which computes gradients I feel I won't get any help from spark-ml.
– Reith
Jan 14 '18 at 11:50
True, although it might sound more serious than it is. After all with GD it is just a one line with any decent linear algebra lib. But if you're looking for built-in support in Structured Streaming there is none at the moment.
– hi-zir
Jan 14 '18 at 11:57
Thanks. Can you elaborate more about forEach method? As I understand It's a method to do some action (like writing to db) on each
Row. But for applying regression I need to do some action (training and evaluating) on a dataset.– Reith
Jan 14 '18 at 11:41
Thanks. Can you elaborate more about forEach method? As I understand It's a method to do some action (like writing to db) on each
Row. But for applying regression I need to do some action (training and evaluating) on a dataset.– Reith
Jan 14 '18 at 11:41
Thanks. I think I'll give up with streaming API since by external storage which computes gradients I feel I won't get any help from spark-ml.
– Reith
Jan 14 '18 at 11:50
Thanks. I think I'll give up with streaming API since by external storage which computes gradients I feel I won't get any help from spark-ml.
– Reith
Jan 14 '18 at 11:50
True, although it might sound more serious than it is. After all with GD it is just a one line with any decent linear algebra lib. But if you're looking for built-in support in Structured Streaming there is none at the moment.
– hi-zir
Jan 14 '18 at 11:57
True, although it might sound more serious than it is. After all with GD it is just a one line with any decent linear algebra lib. But if you're looking for built-in support in Structured Streaming there is none at the moment.
– hi-zir
Jan 14 '18 at 11:57
add a comment |
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