Re: Which is the best way to get a connection to an external database per task in Spark Streaming?
Hi Jerry, it's all clear to me now, I will try with something like Apache
DBCP for the connection pool
Thanks a lot for your help!
2014-07-09 3:08 GMT+02:00 Shao, Saisai saisai.s...@intel.com:
Yes, that would be the Java equivalence to use static class member, but
you should carefully program to prevent resource leakage. A good choice is
to use third-party DB connection library which supports connection pool,
that will alleviate your programming efforts.
Thanks
Jerry
*From:* Juan Rodríguez Hortalá [mailto:juan.rodriguez.hort...@gmail.com]
*Sent:* Tuesday, July 08, 2014 6:54 PM
*To:* user@spark.apache.org
*Subject:* Re: Which is the best way to get a connection to an external
database per task in Spark Streaming?
Hi Jerry, thanks for your answer. I'm using Spark Streaming for Java, and
I only have rudimentary knowledge about Scala, how could I recreate in Java
the lazy creation of a singleton object that you propose for Scala? Maybe a
static class member in Java for the connection would be the solution?
Thanks again for your help,
Best Regards,
Juan
2014-07-08 11:44 GMT+02:00 Shao, Saisai saisai.s...@intel.com:
I think you can maintain a connection pool or keep the connection as a
long-lived object in executor side (like lazily creating a singleton object
in object { } in Scala), so your task can get this connection each time
executing a task, not creating a new one, that would be good for your
scenario, since create a connection is quite expensive for each task.
Thanks
Jerry
*From:* Juan Rodríguez Hortalá [mailto:juan.rodriguez.hort...@gmail.com]
*Sent:* Tuesday, July 08, 2014 5:19 PM
*To:* Tobias Pfeiffer
*Cc:* user@spark.apache.org
*Subject:* Re: Which is the best way to get a connection to an external
database per task in Spark Streaming?
Hi Tobias, thanks for your help. I understand that with that code we
obtain a database connection per partition, but I also suspect that with
that code a new database connection is created per each execution of the
function used as argument for mapPartitions(). That would be very
inefficient because a new object and a new database connection would be
created for each batch of the DStream. But my knowledge about the lifecycle
of Functions in Spark Streaming is very limited, so maybe I'm wrong, what
do you think?
Greetings,
Juan
2014-07-08 3:30 GMT+02:00 Tobias Pfeiffer t...@preferred.jp:
Juan,
I am doing something similar, just not insert into SQL database, but
issue some RPC call. I think mapPartitions() may be helpful to you. You
could do something like
dstream.mapPartitions(iter = {
val db = new DbConnection()
// maybe only do the above if !iter.isEmpty
iter.map(item = {
db.call(...)
// do some cleanup if !iter.hasNext here
item
})
}).count() // force output
Keep in mind though that the whole idea about RDDs is that operations are
idempotent and in theory could be run on multiple hosts (to take the result
from the fastest server) or multiple times (to deal with failures/timeouts)
etc., which is maybe something you want to deal with in your SQL.
Tobias
On Tue, Jul 8, 2014 at 3:40 AM, Juan Rodríguez Hortalá
juan.rodriguez.hort...@gmail.com wrote:
Hi list,
I'm writing a Spark Streaming program that reads from a kafka topic,
performs some transformations on the data, and then inserts each record in
a database with foreachRDD. I was wondering which is the best way to handle
the connection to the database so each worker, or even each task, uses a
different connection to the database, and then database inserts/updates
would be performed in parallel.
- I understand that using a final variable in the driver code is not a
good idea because then the communication with the database would be
performed in the driver code, which leads to a bottleneck, according to
http://engineering.sharethrough.com/blog/2013/09/13/top-3-troubleshooting-tips-to-keep-you-sparking/
- I think creating a new connection in the call() method of the Function
passed to foreachRDD is also a bad idea, because then I wouldn't be reusing
the connection to the database for each batch RDD in the DStream
- I'm not sure that a broadcast variable with the connection handler is a
good idea in case the target database is distributed, because if the same
handler is used for all the nodes of the Spark cluster then than could have
a negative effect in the data locality of the connection to the database.
- From
http://apache-spark-user-list.1001560.n3.nabble.com/Database-connection-per-worker-td1280.html
I understand that by using an static variable and referencing it in the
call() method of the Function passed to foreachRDD we get a different
connection per Spark worker, I guess it's because there is a different JVM
per worker. But then all the tasks in the same worker would share the same
database handler object, am I right?
- Another idea is
Re: Which is the best way to get a connection to an external database per task in Spark Streaming?
Hi Tobias, thanks for your help. I understand that with that code we obtain
a database connection per partition, but I also suspect that with that code
a new database connection is created per each execution of the function
used as argument for mapPartitions(). That would be very inefficient
because a new object and a new database connection would be created for
each batch of the DStream. But my knowledge about the lifecycle of
Functions in Spark Streaming is very limited, so maybe I'm wrong, what do
you think?
Greetings,
Juan
2014-07-08 3:30 GMT+02:00 Tobias Pfeiffer t...@preferred.jp:
Juan,
I am doing something similar, just not insert into SQL database, but
issue some RPC call. I think mapPartitions() may be helpful to you. You
could do something like
dstream.mapPartitions(iter = {
val db = new DbConnection()
// maybe only do the above if !iter.isEmpty
iter.map(item = {
db.call(...)
// do some cleanup if !iter.hasNext here
item
})
}).count() // force output
Keep in mind though that the whole idea about RDDs is that operations are
idempotent and in theory could be run on multiple hosts (to take the result
from the fastest server) or multiple times (to deal with failures/timeouts)
etc., which is maybe something you want to deal with in your SQL.
Tobias
On Tue, Jul 8, 2014 at 3:40 AM, Juan Rodríguez Hortalá
juan.rodriguez.hort...@gmail.com wrote:
Hi list,
I'm writing a Spark Streaming program that reads from a kafka topic,
performs some transformations on the data, and then inserts each record in
a database with foreachRDD. I was wondering which is the best way to handle
the connection to the database so each worker, or even each task, uses a
different connection to the database, and then database inserts/updates
would be performed in parallel.
- I understand that using a final variable in the driver code is not a
good idea because then the communication with the database would be
performed in the driver code, which leads to a bottleneck, according to
http://engineering.sharethrough.com/blog/2013/09/13/top-3-troubleshooting-tips-to-keep-you-sparking/
- I think creating a new connection in the call() method of the Function
passed to foreachRDD is also a bad idea, because then I wouldn't be reusing
the connection to the database for each batch RDD in the DStream
- I'm not sure that a broadcast variable with the connection handler is a
good idea in case the target database is distributed, because if the same
handler is used for all the nodes of the Spark cluster then than could have
a negative effect in the data locality of the connection to the database.
- From
http://apache-spark-user-list.1001560.n3.nabble.com/Database-connection-per-worker-td1280.html
I understand that by using an static variable and referencing it in the
call() method of the Function passed to foreachRDD we get a different
connection per Spark worker, I guess it's because there is a different JVM
per worker. But then all the tasks in the same worker would share the same
database handler object, am I right?
- Another idea is using updateStateByKey() using the database handler as
the state, but I guess that would only work for Serializable database
handlers, and for example not for an org.apache.hadoop.hbase.client.HTable
object.
So my question is, which is the best way to get a connection to an
external database per task in Spark Streaming? Or at least per worker. In
http://apache-spark-user-list.1001560.n3.nabble.com/Connecting-to-an-inmemory-database-from-Spark-td1343.html
there is a partial solution to this question, but there the database
handler object is missing. This other question
http://apache-spark-user-list.1001560.n3.nabble.com/Spark-Streaming-Shared-hashmaps-td3247.html
is closer to mine, but there is no answer for it yet
Thanks in advance,
Greetings,
Juan
RE: Which is the best way to get a connection to an external database per task in Spark Streaming?
I think you can maintain a connection pool or keep the connection as a
long-lived object in executor side (like lazily creating a singleton object in
object { } in Scala), so your task can get this connection each time executing
a task, not creating a new one, that would be good for your scenario, since
create a connection is quite expensive for each task.
Thanks
Jerry
From: Juan Rodríguez Hortalá [mailto:juan.rodriguez.hort...@gmail.com]
Sent: Tuesday, July 08, 2014 5:19 PM
To: Tobias Pfeiffer
Cc: user@spark.apache.org
Subject: Re: Which is the best way to get a connection to an external database
per task in Spark Streaming?
Hi Tobias, thanks for your help. I understand that with that code we obtain a
database connection per partition, but I also suspect that with that code a new
database connection is created per each execution of the function used as
argument for mapPartitions(). That would be very inefficient because a new
object and a new database connection would be created for each batch of the
DStream. But my knowledge about the lifecycle of Functions in Spark Streaming
is very limited, so maybe I'm wrong, what do you think?
Greetings,
Juan
2014-07-08 3:30 GMT+02:00 Tobias Pfeiffer
t...@preferred.jpmailto:t...@preferred.jp:
Juan,
I am doing something similar, just not insert into SQL database, but issue
some RPC call. I think mapPartitions() may be helpful to you. You could do
something like
dstream.mapPartitions(iter = {
val db = new DbConnection()
// maybe only do the above if !iter.isEmpty
iter.map(item = {
db.call(...)
// do some cleanup if !iter.hasNext here
item
})
}).count() // force output
Keep in mind though that the whole idea about RDDs is that operations are
idempotent and in theory could be run on multiple hosts (to take the result
from the fastest server) or multiple times (to deal with failures/timeouts)
etc., which is maybe something you want to deal with in your SQL.
Tobias
On Tue, Jul 8, 2014 at 3:40 AM, Juan Rodríguez Hortalá
juan.rodriguez.hort...@gmail.commailto:juan.rodriguez.hort...@gmail.com
wrote:
Hi list,
I'm writing a Spark Streaming program that reads from a kafka topic, performs
some transformations on the data, and then inserts each record in a database
with foreachRDD. I was wondering which is the best way to handle the connection
to the database so each worker, or even each task, uses a different connection
to the database, and then database inserts/updates would be performed in
parallel.
- I understand that using a final variable in the driver code is not a good
idea because then the communication with the database would be performed in the
driver code, which leads to a bottleneck, according to
http://engineering.sharethrough.com/blog/2013/09/13/top-3-troubleshooting-tips-to-keep-you-sparking/
- I think creating a new connection in the call() method of the Function passed
to foreachRDD is also a bad idea, because then I wouldn't be reusing the
connection to the database for each batch RDD in the DStream
- I'm not sure that a broadcast variable with the connection handler is a good
idea in case the target database is distributed, because if the same handler is
used for all the nodes of the Spark cluster then than could have a negative
effect in the data locality of the connection to the database.
- From
http://apache-spark-user-list.1001560.n3.nabble.com/Database-connection-per-worker-td1280.html
I understand that by using an static variable and referencing it in the call()
method of the Function passed to foreachRDD we get a different connection per
Spark worker, I guess it's because there is a different JVM per worker. But
then all the tasks in the same worker would share the same database handler
object, am I right?
- Another idea is using updateStateByKey() using the database handler as the
state, but I guess that would only work for Serializable database handlers, and
for example not for an org.apache.hadoop.hbase.client.HTable object.
So my question is, which is the best way to get a connection to an external
database per task in Spark Streaming? Or at least per worker. In
http://apache-spark-user-list.1001560.n3.nabble.com/Connecting-to-an-inmemory-database-from-Spark-td1343.html
there is a partial solution to this question, but there the database handler
object is missing. This other question
http://apache-spark-user-list.1001560.n3.nabble.com/Spark-Streaming-Shared-hashmaps-td3247.html
is closer to mine, but there is no answer for it yet
Thanks in advance,
Greetings,
Juan
Re: Which is the best way to get a connection to an external database per task in Spark Streaming?
Hi Jerry, thanks for your answer. I'm using Spark Streaming for Java, and I
only have rudimentary knowledge about Scala, how could I recreate in Java
the lazy creation of a singleton object that you propose for Scala? Maybe a
static class member in Java for the connection would be the solution?
Thanks again for your help,
Best Regards,
Juan
2014-07-08 11:44 GMT+02:00 Shao, Saisai saisai.s...@intel.com:
I think you can maintain a connection pool or keep the connection as a
long-lived object in executor side (like lazily creating a singleton object
in object { } in Scala), so your task can get this connection each time
executing a task, not creating a new one, that would be good for your
scenario, since create a connection is quite expensive for each task.
Thanks
Jerry
*From:* Juan Rodríguez Hortalá [mailto:juan.rodriguez.hort...@gmail.com]
*Sent:* Tuesday, July 08, 2014 5:19 PM
*To:* Tobias Pfeiffer
*Cc:* user@spark.apache.org
*Subject:* Re: Which is the best way to get a connection to an external
database per task in Spark Streaming?
Hi Tobias, thanks for your help. I understand that with that code we
obtain a database connection per partition, but I also suspect that with
that code a new database connection is created per each execution of the
function used as argument for mapPartitions(). That would be very
inefficient because a new object and a new database connection would be
created for each batch of the DStream. But my knowledge about the lifecycle
of Functions in Spark Streaming is very limited, so maybe I'm wrong, what
do you think?
Greetings,
Juan
2014-07-08 3:30 GMT+02:00 Tobias Pfeiffer t...@preferred.jp:
Juan,
I am doing something similar, just not insert into SQL database, but
issue some RPC call. I think mapPartitions() may be helpful to you. You
could do something like
dstream.mapPartitions(iter = {
val db = new DbConnection()
// maybe only do the above if !iter.isEmpty
iter.map(item = {
db.call(...)
// do some cleanup if !iter.hasNext here
item
})
}).count() // force output
Keep in mind though that the whole idea about RDDs is that operations are
idempotent and in theory could be run on multiple hosts (to take the result
from the fastest server) or multiple times (to deal with failures/timeouts)
etc., which is maybe something you want to deal with in your SQL.
Tobias
On Tue, Jul 8, 2014 at 3:40 AM, Juan Rodríguez Hortalá
juan.rodriguez.hort...@gmail.com wrote:
Hi list,
I'm writing a Spark Streaming program that reads from a kafka topic,
performs some transformations on the data, and then inserts each record in
a database with foreachRDD. I was wondering which is the best way to handle
the connection to the database so each worker, or even each task, uses a
different connection to the database, and then database inserts/updates
would be performed in parallel.
- I understand that using a final variable in the driver code is not a
good idea because then the communication with the database would be
performed in the driver code, which leads to a bottleneck, according to
http://engineering.sharethrough.com/blog/2013/09/13/top-3-troubleshooting-tips-to-keep-you-sparking/
- I think creating a new connection in the call() method of the Function
passed to foreachRDD is also a bad idea, because then I wouldn't be reusing
the connection to the database for each batch RDD in the DStream
- I'm not sure that a broadcast variable with the connection handler is a
good idea in case the target database is distributed, because if the same
handler is used for all the nodes of the Spark cluster then than could have
a negative effect in the data locality of the connection to the database.
- From
http://apache-spark-user-list.1001560.n3.nabble.com/Database-connection-per-worker-td1280.html
I understand that by using an static variable and referencing it in the
call() method of the Function passed to foreachRDD we get a different
connection per Spark worker, I guess it's because there is a different JVM
per worker. But then all the tasks in the same worker would share the same
database handler object, am I right?
- Another idea is using updateStateByKey() using the database handler as
the state, but I guess that would only work for Serializable database
handlers, and for example not for an org.apache.hadoop.hbase.client.HTable
object.
So my question is, which is the best way to get a connection to an
external database per task in Spark Streaming? Or at least per worker. In
http://apache-spark-user-list.1001560.n3.nabble.com/Connecting-to-an-inmemory-database-from-Spark-td1343.html
there is a partial solution to this question, but there the database
handler object is missing. This other question
http://apache-spark-user-list.1001560.n3.nabble.com/Spark-Streaming-Shared-hashmaps-td3247.html
is closer to mine, but there is no answer for it yet
Thanks in advance,
RE: Which is the best way to get a connection to an external database per task in Spark Streaming?
Yes, that would be the Java equivalence to use static class member, but you
should carefully program to prevent resource leakage. A good choice is to use
third-party DB connection library which supports connection pool, that will
alleviate your programming efforts.
Thanks
Jerry
From: Juan Rodríguez Hortalá [mailto:juan.rodriguez.hort...@gmail.com]
Sent: Tuesday, July 08, 2014 6:54 PM
To: user@spark.apache.org
Subject: Re: Which is the best way to get a connection to an external database
per task in Spark Streaming?
Hi Jerry, thanks for your answer. I'm using Spark Streaming for Java, and I
only have rudimentary knowledge about Scala, how could I recreate in Java the
lazy creation of a singleton object that you propose for Scala? Maybe a static
class member in Java for the connection would be the solution?
Thanks again for your help,
Best Regards,
Juan
2014-07-08 11:44 GMT+02:00 Shao, Saisai
saisai.s...@intel.commailto:saisai.s...@intel.com:
I think you can maintain a connection pool or keep the connection as a
long-lived object in executor side (like lazily creating a singleton object in
object { } in Scala), so your task can get this connection each time executing
a task, not creating a new one, that would be good for your scenario, since
create a connection is quite expensive for each task.
Thanks
Jerry
From: Juan Rodríguez Hortalá
[mailto:juan.rodriguez.hort...@gmail.commailto:juan.rodriguez.hort...@gmail.com]
Sent: Tuesday, July 08, 2014 5:19 PM
To: Tobias Pfeiffer
Cc: user@spark.apache.orgmailto:user@spark.apache.org
Subject: Re: Which is the best way to get a connection to an external database
per task in Spark Streaming?
Hi Tobias, thanks for your help. I understand that with that code we obtain a
database connection per partition, but I also suspect that with that code a new
database connection is created per each execution of the function used as
argument for mapPartitions(). That would be very inefficient because a new
object and a new database connection would be created for each batch of the
DStream. But my knowledge about the lifecycle of Functions in Spark Streaming
is very limited, so maybe I'm wrong, what do you think?
Greetings,
Juan
2014-07-08 3:30 GMT+02:00 Tobias Pfeiffer
t...@preferred.jpmailto:t...@preferred.jp:
Juan,
I am doing something similar, just not insert into SQL database, but issue
some RPC call. I think mapPartitions() may be helpful to you. You could do
something like
dstream.mapPartitions(iter = {
val db = new DbConnection()
// maybe only do the above if !iter.isEmpty
iter.map(item = {
db.call(...)
// do some cleanup if !iter.hasNext here
item
})
}).count() // force output
Keep in mind though that the whole idea about RDDs is that operations are
idempotent and in theory could be run on multiple hosts (to take the result
from the fastest server) or multiple times (to deal with failures/timeouts)
etc., which is maybe something you want to deal with in your SQL.
Tobias
On Tue, Jul 8, 2014 at 3:40 AM, Juan Rodríguez Hortalá
juan.rodriguez.hort...@gmail.commailto:juan.rodriguez.hort...@gmail.com
wrote:
Hi list,
I'm writing a Spark Streaming program that reads from a kafka topic, performs
some transformations on the data, and then inserts each record in a database
with foreachRDD. I was wondering which is the best way to handle the connection
to the database so each worker, or even each task, uses a different connection
to the database, and then database inserts/updates would be performed in
parallel.
- I understand that using a final variable in the driver code is not a good
idea because then the communication with the database would be performed in the
driver code, which leads to a bottleneck, according to
http://engineering.sharethrough.com/blog/2013/09/13/top-3-troubleshooting-tips-to-keep-you-sparking/
- I think creating a new connection in the call() method of the Function passed
to foreachRDD is also a bad idea, because then I wouldn't be reusing the
connection to the database for each batch RDD in the DStream
- I'm not sure that a broadcast variable with the connection handler is a good
idea in case the target database is distributed, because if the same handler is
used for all the nodes of the Spark cluster then than could have a negative
effect in the data locality of the connection to the database.
- From
http://apache-spark-user-list.1001560.n3.nabble.com/Database-connection-per-worker-td1280.html
I understand that by using an static variable and referencing it in the call()
method of the Function passed to foreachRDD we get a different connection per
Spark worker, I guess it's because there is a different JVM per worker. But
then all the tasks in the same worker would share the same database handler
object, am I right?
- Another idea is using updateStateByKey() using the database handler as the
state, but I guess that would only work for Serializable database handlers, and
for
Re: Which is the best way to get a connection to an external database per task in Spark Streaming?
Juan,
I am doing something similar, just not insert into SQL database, but
issue some RPC call. I think mapPartitions() may be helpful to you. You
could do something like
dstream.mapPartitions(iter = {
val db = new DbConnection()
// maybe only do the above if !iter.isEmpty
iter.map(item = {
db.call(...)
// do some cleanup if !iter.hasNext here
item
})
}).count() // force output
Keep in mind though that the whole idea about RDDs is that operations are
idempotent and in theory could be run on multiple hosts (to take the result
from the fastest server) or multiple times (to deal with failures/timeouts)
etc., which is maybe something you want to deal with in your SQL.
Tobias
On Tue, Jul 8, 2014 at 3:40 AM, Juan Rodríguez Hortalá
juan.rodriguez.hort...@gmail.com wrote:
Hi list,
I'm writing a Spark Streaming program that reads from a kafka topic,
performs some transformations on the data, and then inserts each record in
a database with foreachRDD. I was wondering which is the best way to handle
the connection to the database so each worker, or even each task, uses a
different connection to the database, and then database inserts/updates
would be performed in parallel.
- I understand that using a final variable in the driver code is not a
good idea because then the communication with the database would be
performed in the driver code, which leads to a bottleneck, according to
http://engineering.sharethrough.com/blog/2013/09/13/top-3-troubleshooting-tips-to-keep-you-sparking/
- I think creating a new connection in the call() method of the Function
passed to foreachRDD is also a bad idea, because then I wouldn't be reusing
the connection to the database for each batch RDD in the DStream
- I'm not sure that a broadcast variable with the connection handler is a
good idea in case the target database is distributed, because if the same
handler is used for all the nodes of the Spark cluster then than could have
a negative effect in the data locality of the connection to the database.
- From
http://apache-spark-user-list.1001560.n3.nabble.com/Database-connection-per-worker-td1280.html
I understand that by using an static variable and referencing it in the
call() method of the Function passed to foreachRDD we get a different
connection per Spark worker, I guess it's because there is a different JVM
per worker. But then all the tasks in the same worker would share the same
database handler object, am I right?
- Another idea is using updateStateByKey() using the database handler as
the state, but I guess that would only work for Serializable database
handlers, and for example not for an org.apache.hadoop.hbase.client.HTable
object.
So my question is, which is the best way to get a connection to an
external database per task in Spark Streaming? Or at least per worker. In
http://apache-spark-user-list.1001560.n3.nabble.com/Connecting-to-an-inmemory-database-from-Spark-td1343.html
there is a partial solution to this question, but there the database
handler object is missing. This other question
http://apache-spark-user-list.1001560.n3.nabble.com/Spark-Streaming-Shared-hashmaps-td3247.html
is closer to mine, but there is no answer for it yet
Thanks in advance,
Greetings,
Juan
