Transparent cache when querying time series with Apache Spark
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We have time series data, as daily parquet file of 3 GB in HDFS (hdfs:///data/year=X/month=X/day=X/data.parquet.gz), warehouse'd by Hive as data table.
All night, we run SQL queries to generate reports, with Apache Spark:
(1) SELECT date, count(*) from data GROUP BY date
(of course we have more complex query ^^)
I notice Apache Spark will run the query on all our data set (which is normal), but I would like to re-use data of previous day if possible since the previous data never change.
Solution in place
I can achieve this by doing an incremental consolidation:
(2) INSERT INTO consolidation SELECT date, count(*) FROM data WHERE date="yesterday"
then run the query against it ((3) SELECT date, value FROM consolidation)
Transparent cache I want
I am wondering if it's possible to have this behavior with the query (1) maybe by hacking how Spark is generating the logical plan, I dont know.
apache-spark apache-spark-sql
edited Nov 10 at 13:12
cricket_007
78.6k1142109
asked Nov 10 at 9:18
Thomas Decaux
12.6k25660
add a comment |
up vote
2
down vote
favorite
We have time series data, as daily parquet file of 3 GB in HDFS (hdfs:///data/year=X/month=X/day=X/data.parquet.gz), warehouse'd by Hive as data table.
All night, we run SQL queries to generate reports, with Apache Spark:
(1) SELECT date, count(*) from data GROUP BY date
(of course we have more complex query ^^)
I notice Apache Spark will run the query on all our data set (which is normal), but I would like to re-use data of previous day if possible since the previous data never change.
Solution in place
I can achieve this by doing an incremental consolidation:
(2) INSERT INTO consolidation SELECT date, count(*) FROM data WHERE date="yesterday"
then run the query against it ((3) SELECT date, value FROM consolidation)
Transparent cache I want
I am wondering if it's possible to have this behavior with the query (1) maybe by hacking how Spark is generating the logical plan, I dont know.
apache-spark apache-spark-sql
edited Nov 10 at 13:12
cricket_007
78.6k1142109
asked Nov 10 at 9:18
Thomas Decaux
12.6k25660
1
What you want is a materialized view and requires (a) massive R&D efforts and (b) a way to detect which segments of data have changed since large materialization. Since Spark is not a DBMS and cannot ensure the data files have not changed in the meantime, it can't do it. For the record, Horton (RiP) tries to introduce that feature in Hive 3 but only for "ACID tables" that are under full control of Hive. For the record also, the MVs in Oracle have been there for a decade and are still a bit tricky to work with...
– Samson Scharfrichter
Nov 10 at 10:40
Some questions: 1. Is each days data appended to previous data or each wriiten to separate files? 2. Can you change the writing application? 3. Do you just need data for 2 days together or any range? 4. Can you use the data frame API?
– ookboy24
Nov 10 at 16:41
If you have count distinct over periods greater than one day, reusing pre-aggregated results from previous days would be problematic.
– alexeipab
Nov 10 at 18:43
Data IS appended in new folder, previous data never changed , thats why i think its possible tout achieve this "transparently"
– Thomas Decaux
Nov 12 at 13:43
add a comment |
up vote
2
down vote
favorite
up vote
2
down vote
favorite
We have time series data, as daily parquet file of 3 GB in HDFS (hdfs:///data/year=X/month=X/day=X/data.parquet.gz), warehouse'd by Hive as data table.
All night, we run SQL queries to generate reports, with Apache Spark:
(1) SELECT date, count(*) from data GROUP BY date
(of course we have more complex query ^^)
I notice Apache Spark will run the query on all our data set (which is normal), but I would like to re-use data of previous day if possible since the previous data never change.
Solution in place
I can achieve this by doing an incremental consolidation:
(2) INSERT INTO consolidation SELECT date, count(*) FROM data WHERE date="yesterday"
then run the query against it ((3) SELECT date, value FROM consolidation)
Transparent cache I want
I am wondering if it's possible to have this behavior with the query (1) maybe by hacking how Spark is generating the logical plan, I dont know.
apache-spark apache-spark-sql
edited Nov 10 at 13:12
cricket_007
78.6k1142109
asked Nov 10 at 9:18
Thomas Decaux
12.6k25660
We have time series data, as daily parquet file of 3 GB in HDFS (hdfs:///data/year=X/month=X/day=X/data.parquet.gz), warehouse'd by Hive as data table.
All night, we run SQL queries to generate reports, with Apache Spark:
(1) SELECT date, count(*) from data GROUP BY date
(of course we have more complex query ^^)
I notice Apache Spark will run the query on all our data set (which is normal), but I would like to re-use data of previous day if possible since the previous data never change.
Solution in place
I can achieve this by doing an incremental consolidation:
(2) INSERT INTO consolidation SELECT date, count(*) FROM data WHERE date="yesterday"
then run the query against it ((3) SELECT date, value FROM consolidation)
Transparent cache I want
I am wondering if it's possible to have this behavior with the query (1) maybe by hacking how Spark is generating the logical plan, I dont know.
apache-spark apache-spark-sql
apache-spark apache-spark-sql
edited Nov 10 at 13:12
cricket_007
78.6k1142109
asked Nov 10 at 9:18
Thomas Decaux
12.6k25660
edited Nov 10 at 13:12
cricket_007
78.6k1142109
asked Nov 10 at 9:18
Thomas Decaux
12.6k25660
edited Nov 10 at 13:12
cricket_007
78.6k1142109
edited Nov 10 at 13:12
cricket_007
78.6k1142109
edited Nov 10 at 13:12
cricket_007
78.6k1142109
78.6k1142109
asked Nov 10 at 9:18
Thomas Decaux
12.6k25660
asked Nov 10 at 9:18
Thomas Decaux
12.6k25660
asked Nov 10 at 9:18
Thomas Decaux
12.6k25660
12.6k25660
1
What you want is a materialized view and requires (a) massive R&D efforts and (b) a way to detect which segments of data have changed since large materialization. Since Spark is not a DBMS and cannot ensure the data files have not changed in the meantime, it can't do it. For the record, Horton (RiP) tries to introduce that feature in Hive 3 but only for "ACID tables" that are under full control of Hive. For the record also, the MVs in Oracle have been there for a decade and are still a bit tricky to work with...
– Samson Scharfrichter
Nov 10 at 10:40
Some questions: 1. Is each days data appended to previous data or each wriiten to separate files? 2. Can you change the writing application? 3. Do you just need data for 2 days together or any range? 4. Can you use the data frame API?
– ookboy24
Nov 10 at 16:41
If you have count distinct over periods greater than one day, reusing pre-aggregated results from previous days would be problematic.
– alexeipab
Nov 10 at 18:43
Data IS appended in new folder, previous data never changed , thats why i think its possible tout achieve this "transparently"
– Thomas Decaux
Nov 12 at 13:43
add a comment |
1
What you want is a materialized view and requires (a) massive R&D efforts and (b) a way to detect which segments of data have changed since large materialization. Since Spark is not a DBMS and cannot ensure the data files have not changed in the meantime, it can't do it. For the record, Horton (RiP) tries to introduce that feature in Hive 3 but only for "ACID tables" that are under full control of Hive. For the record also, the MVs in Oracle have been there for a decade and are still a bit tricky to work with...
– Samson Scharfrichter
Nov 10 at 10:40
Some questions: 1. Is each days data appended to previous data or each wriiten to separate files? 2. Can you change the writing application? 3. Do you just need data for 2 days together or any range? 4. Can you use the data frame API?
– ookboy24
Nov 10 at 16:41
If you have count distinct over periods greater than one day, reusing pre-aggregated results from previous days would be problematic.
– alexeipab
Nov 10 at 18:43
Data IS appended in new folder, previous data never changed , thats why i think its possible tout achieve this "transparently"
– Thomas Decaux
Nov 12 at 13:43
1
1
What you want is a materialized view and requires (a) massive R&D efforts and (b) a way to detect which segments of data have changed since large materialization. Since Spark is not a DBMS and cannot ensure the data files have not changed in the meantime, it can't do it. For the record, Horton (RiP) tries to introduce that feature in Hive 3 but only for "ACID tables" that are under full control of Hive. For the record also, the MVs in Oracle have been there for a decade and are still a bit tricky to work with...
– Samson Scharfrichter
Nov 10 at 10:40
What you want is a materialized view and requires (a) massive R&D efforts and (b) a way to detect which segments of data have changed since large materialization. Since Spark is not a DBMS and cannot ensure the data files have not changed in the meantime, it can't do it. For the record, Horton (RiP) tries to introduce that feature in Hive 3 but only for "ACID tables" that are under full control of Hive. For the record also, the MVs in Oracle have been there for a decade and are still a bit tricky to work with...
– Samson Scharfrichter
Nov 10 at 10:40
Some questions: 1. Is each days data appended to previous data or each wriiten to separate files? 2. Can you change the writing application? 3. Do you just need data for 2 days together or any range? 4. Can you use the data frame API?
– ookboy24
Nov 10 at 16:41
Some questions: 1. Is each days data appended to previous data or each wriiten to separate files? 2. Can you change the writing application? 3. Do you just need data for 2 days together or any range? 4. Can you use the data frame API?
– ookboy24
Nov 10 at 16:41
If you have count distinct over periods greater than one day, reusing pre-aggregated results from previous days would be problematic.
– alexeipab
Nov 10 at 18:43
If you have count distinct over periods greater than one day, reusing pre-aggregated results from previous days would be problematic.
– alexeipab
Nov 10 at 18:43
Data IS appended in new folder, previous data never changed , thats why i think its possible tout achieve this "transparently"
– Thomas Decaux
Nov 12 at 13:43
Data IS appended in new folder, previous data never changed , thats why i think its possible tout achieve this "transparently"
– Thomas Decaux
Nov 12 at 13:43
add a comment |
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1
What you want is a materialized view and requires (a) massive R&D efforts and (b) a way to detect which segments of data have changed since large materialization. Since Spark is not a DBMS and cannot ensure the data files have not changed in the meantime, it can't do it. For the record, Horton (RiP) tries to introduce that feature in Hive 3 but only for "ACID tables" that are under full control of Hive. For the record also, the MVs in Oracle have been there for a decade and are still a bit tricky to work with...
– Samson Scharfrichter
Nov 10 at 10:40
Some questions: 1. Is each days data appended to previous data or each wriiten to separate files? 2. Can you change the writing application? 3. Do you just need data for 2 days together or any range? 4. Can you use the data frame API?
– ookboy24
Nov 10 at 16:41
If you have count distinct over periods greater than one day, reusing pre-aggregated results from previous days would be problematic.
– alexeipab
Nov 10 at 18:43
Data IS appended in new folder, previous data never changed , thats why i think its possible tout achieve this "transparently"
– Thomas Decaux
Nov 12 at 13:43