Hi.
This is a very good question
I did some tests on this.
If you are joining two tables then you are creating a result set based on
some conditions. In this case what I normally do is to specify an ID column
from either tables and will base my partitioning on that ID column. This is
pretty straight forward. So bring back your ID column and base you lower
and upper limit on that ID value
"partitionColumn" -> "ID",
"lowerBound" -> "1",
"upperBound" -> "100000000",
"numPartitions" -> "100",
Also I have noticed that regardless of the number of partitions you specify
at the RDBMS site, the number of parallel connections will be limited and
the result set will be partitioned accordingly. For example with
numberPartitions=100, I see only 8 connections in Oracle coming from Spark
connection.
scala> val s = HiveContext.read.format("jdbc").options(
| Map("url" -> _ORACLEserver,
| "dbtable" -> "(SELECT to_char(ID) AS ID, to_char(CLUSTERED) AS
CLUSTERED, to_char(SCATTERED) AS SCATTERED, to_char(RANDOMISED) AS
RANDOMISED, RANDOM_STRING, SMALL_VC, PADDING FROM scratchpad.dummy)",
| "partitionColumn" -> "ID",
| "lowerBound" -> "1",
| "upperBound" -> "100000000",
| "numPartitions" -> "100",
| "user" -> _username,
| "password" -> _password)).load
s: org.apache.spark.sql.DataFrame = [ID: string, CLUSTERED: string ... 5
more fields]
scala> s.toJavaRDD.partitions.size()
res1: Int = 100
This also seems to set the number of partitions. I still think that the
emphasis has to be on getting data from RDBMS as quickly as possible. The
partitioning does work. In below the login scratchpad has multiple
connections to Oracle and does the range selection OK
1 SCRATCHPAD 45 43048 1 SELECT
"SMALL_VC","CLUSTERED","PADDING","RANDOM_ST
RING","ID","SCATTERED","RANDOMISED" FROM (SELECT t
o_char(ID) AS ID,
to_char(CLUSTERED) AS CLUSTERED,
to_char(SCATTERED)
AS SCATTERED, to_char(RANDOMIS
ED) AS RANDOMISED,
RANDOM_STRING, SMALL_VC, PADDIN
G FROM
scratchpad.dummy)
*WHERE ID >= 16000001
AND ID < 17000001*
HTH
Dr Mich Talebzadeh
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On 15 August 2016 at 08:18, ayan guha <guha.a...@gmail.com> wrote:
> Hi
>
> I would suggest you to look at sqoop as well. Essentially, you can provide
> a splitBy/partitionBy column using which data will be distributed among
> your stated number of mappers
>
> On Mon, Aug 15, 2016 at 5:07 PM, Madabhattula Rajesh Kumar <
> mrajaf...@gmail.com> wrote:
>
>> Hi Mich,
>>
>> I have a below question.
>>
>> I want to join two tables and return the result based on the input value.
>> In this case, how we need to specify lower bound and upper bound values ?
>>
>> select t1.id, t1.name, t2.course, t2.qualification from t1, t2 where
>> t1.transactionid=*11111* and t1.id = t2.id
>>
>> *11111 => dynamic input value.*
>>
>> Regards,
>> Rajesh
>>
>> On Mon, Aug 15, 2016 at 12:05 PM, Mich Talebzadeh <
>> mich.talebza...@gmail.com> wrote:
>>
>>> If you have your RDBMS table partitioned, then you need to consider how
>>> much data you want to extract in other words the result set returned by the
>>> JDBC call.
>>>
>>> If you want all the data, then the number of partitions specified in the
>>> JDBC call should be equal to the number of partitions in your RDBMS table.
>>>
>>> HTH
>>>
>>> Dr Mich Talebzadeh
>>>
>>>
>>>
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>>>
>>> On 14 August 2016 at 21:44, Ashok Kumar <ashok34...@yahoo.com> wrote:
>>>
>>>> Thank you very much sir.
>>>>
>>>> I forgot to mention that two of these Oracle tables are range
>>>> partitioned. In that case what would be the optimum number of partitions if
>>>> you can share?
>>>>
>>>> Warmest
>>>>
>>>>
>>>> On Sunday, 14 August 2016, 21:37, Mich Talebzadeh <
>>>> mich.talebza...@gmail.com> wrote:
>>>>
>>>>
>>>> If you have primary keys on these tables then you can parallelise the
>>>> process reading data.
>>>>
>>>> You have to be careful not to set the number of partitions too many.
>>>> Certainly there is a balance between the number of partitions supplied to
>>>> JDBC and the load on the network and the source DB.
>>>>
>>>> Assuming that your underlying table has primary key ID, then this will
>>>> create 20 parallel processes to Oracle DB
>>>>
>>>> val d = HiveContext.read.format("jdbc").options(
>>>> Map("url" -> _ORACLEserver,
>>>> "dbtable" -> "(SELECT <COL1>, <COL2>, ....FROM <TABLE>)",
>>>> "partitionColumn" -> "ID",
>>>> "lowerBound" -> "1",
>>>> "upperBound" -> "maxID",
>>>> "numPartitions" -> "20",
>>>> "user" -> _username,
>>>> "password" -> _password)).load
>>>>
>>>> assuming your upper bound on ID is maxID
>>>>
>>>>
>>>> This will open multiple connections to RDBMS, each getting a subset of
>>>> data that you want.
>>>>
>>>> You need to test it to ensure that you get the numPartitions optimum
>>>> and you don't overload any component.
>>>>
>>>> HTH
>>>>
>>>>
>>>> Dr Mich Talebzadeh
>>>>
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>>>> disclaimed. The author will in no case be liable for any monetary damages
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>>>>
>>>>
>>>> On 14 August 2016 at 21:15, Ashok Kumar <ashok34...@yahoo.com.invalid>
>>>> wrote:
>>>>
>>>> Hi,
>>>>
>>>> There are 4 tables ranging from 10 million to 100 million rows but they
>>>> all have primary keys.
>>>>
>>>> The network is fine but our Oracle is RAC and we can only connect to a
>>>> designated Oracle node (where we have a DQ account only).
>>>>
>>>> We have a limited time window of few hours to get the required data out.
>>>>
>>>> Thanks
>>>>
>>>>
>>>> On Sunday, 14 August 2016, 21:07, Mich Talebzadeh <
>>>> mich.talebza...@gmail.com> wrote:
>>>>
>>>>
>>>> How big are your tables and is there any issue with the network between
>>>> your Spark nodes and your Oracle DB that adds to issues?
>>>>
>>>> HTH
>>>>
>>>> Dr Mich Talebzadeh
>>>>
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>>>> AAEAAAAWh2gBxianrbJd6zP6AcPCCd OABUrV8Pw
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>>>>
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>>>> any loss, damage or destruction of data or any other property which may
>>>> arise from relying on this email's technical content is explicitly
>>>> disclaimed. The author will in no case be liable for any monetary damages
>>>> arising from such loss, damage or destruction.
>>>>
>>>>
>>>> On 14 August 2016 at 20:50, Ashok Kumar <ashok34...@yahoo.com.invalid>
>>>> wrote:
>>>>
>>>> Hi Gurus,
>>>>
>>>> I have few large tables in rdbms (ours is Oracle). We want to access
>>>> these tables through Spark JDBC
>>>>
>>>> What is the quickest way of getting data into Spark Dataframe say
>>>> multiple connections from Spark
>>>>
>>>> thanking you
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>
>>
>
>
> --
> Best Regards,
> Ayan Guha
>
