Actually I found a solution to this issue
*Challenge*
Insert data from Spark dataframe when one or more columns in theOracle
table rely on some derived_colums dependent on data in one or more
dataframe columns.
Standard JDBC from Spark to Oracle does batch insert of dataframe into
Oracle *so it cannot handle these derived columns*. Refer below
*dataFrame.* \
write. \
format("jdbc"). \
option("url", url of Oracle). \
*option("dbtable", schema.tableName)*. \
option("user", user). \
option("password", password). \
option("driver", Oracle driver). \
mode(mode). \
*save()*
This writes the whole content of the dataframe to the Oracle table. Cannot
replace schema.tableName with INSERT statement
*Possible solution*
1. Need a cursor based solution. Create a cursor from Spark dataframe.
So we can walk through every row and get the value of each column from the
dataframe
2. Oracle provides the cx_Oracle package. cx_Oracle
<https://oracle.github.io/python-cx_Oracle/> is a Python extension
module that enables access to Oracle Database. It conforms to the Python
database API 2.0 specification
<http://www.python.org/topics/database/DatabaseAPI-2.0.html> with a
considerable number of additions and a couple of exclusions. It is
maintained by Oracle.
3. Using cx_Oracle we should be able to create a Connection type to
Oracle and use Connection.cursor() to deal with rows. See below
This is an example
Create connection to Oracle. Need to install cx_oracle package in PySpark
import cx_Oracle
def loadIntoOracleTableWithCursor(self, df):
# set Oracle details
tableName = "randomdata"
fullyQualifiedTableName =
self.config['OracleVariables']['dbschema']+'.'+tableName
user = self.config['OracleVariables']['oracle_user']
password = self.config['OracleVariables']['oracle_password']
serverName = self.config['OracleVariables']['oracleHost']
port = self.config['OracleVariables']['oraclePort']
serviceName = self.config['OracleVariables']['serviceName']
dsn_tns = cx_Oracle.makedsn(serverName, port,
service_name=serviceName)
# create connection conn
conn = cx_Oracle.connect(user, password, dsn_tns)
cursor = conn.cursor()
# df is the dataframe containing the data. Let us build a cursor on
it.
for row in df.rdd.collect():
# get individual column values from the dataframe
id = row[0]
clustered = row[1]
scattered = row[2]
randomised = row[3]
random_string = row[4]
small_vc = row[5]
padding = row[6]
# Build INSERT/SELECT statement to be executed in Oracle. This
is what we are sending for every row to the Oracle table. Oracle table has
a column called *derived_col *that dataframe does not have it.
# That is the one that is derived from some value on
the dataframe column(s). For example here I assign *derived_col = cos(id)*
and pass it in sqlText. You need {} to pass the value and enclose i single
quotes
# if the column is character type
sqlText = f"""insert into {fullyQualifiedTableName}
(id,clustered,scattered,randomised,random_string,small_vc,padding,
*derived_col)*
values
({id},{clustered},{scattered},{randomised},'{random_string}','{small_vc}','{padding}',
*cos({id*}))"""
print(sqlText)
cursor.execute(sqlText)
conn.commit()
Our dataframe has 10 rows and id in Oracle table has been made the primary
key
scratch...@orasource.mich.LOCAL> CREATE TABLE scratchpad.randomdata
2 (
3 "ID" NUMBER(*,0),
4 "CLUSTERED" NUMBER(*,0),
5 "SCATTERED" NUMBER(*,0),
6 "RANDOMISED" NUMBER(*,0),
7 "RANDOM_STRING" VARCHAR2(50 BYTE),
8 "SMALL_VC" VARCHAR2(50 BYTE),
9 "PADDING" VARCHAR2(4000 BYTE),
10 "DERIVED_COL" FLOAT(126)
11 );
Table created.
scratch...@orasource.mich.LOCAL> ALTER TABLE scratchpad.randomdata ADD
CONSTRAINT randomdata_PK PRIMARY KEY (ID);
Table altered.
Run it and see the output of print(sqlText)
insert into SCRATCHPAD.randomdata
(id,clustered,scattered,randomised,random_string,small_vc,padding,derived_col)
values
(1,0.0,0.0,2.0,'KZWeqhFWCEPyYngFbyBMWXaSCrUZoLgubbbPIayRnBUbHoWCFJ','
1','xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx',cos(1))
This works fine. It creates the rows and does a commit
In Oracle confirm those 10 rows added starting with id = 1
scratch...@orasource.mich.LOCAL> select count(1) from scratchpad.randomdata;
COUNT(1)
----------
10
If you repeat the spark code again you will get primary key constraint
violation in Oracle and rows will be rejected
cx_Oracle.IntegrityError: ORA-00001: unique constraint
(SCRATCHPAD.RANDOMDATA_PK) violated
HTH
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On Sat, 19 Jun 2021 at 20:01, Sebastian Piu <sebastian....@gmail.com> wrote:
> Another option is to just use plain jdbc (if in java) in a
> foreachPartition call on the dataframe/dataset then you get full control of
> the insert statement but need to open the connection/transaction yourself
>
> On Sat, 19 Jun 2021 at 19:33, Mich Talebzadeh <mich.talebza...@gmail.com>
> wrote:
>
>> Hi,
>>
>> I did some research on this.
>>
>> The only way one can write to Oracle from Spark is through JDBC
>> (excluding other options outside of Spark).
>>
>> The challenge here is that you have a column based on function
>> get_function() column that Spark needs to insert. Currently there is no
>> way of inserting records from Park using the traditional INSERT SELECT
>> statement. For example this does not work through Spark
>>
>> scratch...@orasource.mich.LOCAL> insert into scratchpad.dummy6 (id)
>> values (2);
>>
>> The batch insert option seems to be fastest
>>
>> df.write. \
>> format("jdbc"). \
>> option("url", oracle_url). \
>> option("user", user). \
>> option("dbtable", "scratchpad.randomdata"). \ # you
>> cannot replace this with sql insert!
>> option("password", password). \
>> option("driver", driver). \
>> mode(mode). \
>> save()
>>
>> How about creating a cursor on DF
>>
>> for row in df.rdd.collect():
>> id = row[0]
>> clustered = row[1]
>> scattered = row[2]
>> randomised = row[3]
>> random_string = row[4]
>> small_vc = row[5]
>> padding= row[6]
>>
>> This will print out the individual column values row by row from the
>> dataframe but cannot do much about it
>>
>> The only option I can see here is to create a staging table EXCLUDING the
>> derived column and write to that table.
>>
>> Next go to Oracle itself and do an insert/select from the staging table
>> to the target table. Let us create table dumm7 in the image of the one
>> created by spark
>>
>> scratch...@orasource.mich.LOCAL> create table scratchpad.dummy7 as
>> select * from scratchpad.randomdata where 1 = 2;
>>
>> Table created.
>>
>> Add a new derived column to it, call it derived_col
>>
>> scratch...@orasource.mich.LOCAL> alter table scratchpad.dummy7 add
>> derived_col float;
>>
>> Table altered.
>>
>> Now insert/select from scratchpad.randomdata to scratchpad.dummy7. Let us
>> populate the new added column with cos(id)
>>
>> scratch...@orasource.mich.LOCAL> insert into scratchpad.dummy7 (id,
>> CLUSTERED, SCATTERED, RANDOMISED, RANDOM_STRING, SMALL_VC, PADDING,
>> DERIVED_COL)
>> 2 select id, CLUSTERED, SCATTERED, RANDOMISED, RANDOM_STRING,
>> SMALL_VC, PADDING, *cos(id)* from randomdata;
>>
>> 10 rows created.
>>
>> This should work, unless there is a way of inserting columns directly
>> from Spark.
>>
>> HTH
>>
>>
>>
>> view my Linkedin profile
>> <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>
>>
>>
>>
>> *Disclaimer:* Use it at your own risk. Any and all responsibility for
>> 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 Fri, 18 Jun 2021 at 22:14, Mich Talebzadeh <mich.talebza...@gmail.com>
>> wrote:
>>
>>> Well the challenge is that Spark is best suited to insert a dataframe
>>> into the Oracle table, i.e. a bulk insert
>>>
>>> that insert into table (column list) values (..) is a single record
>>> insert .. Can you try creating a staging table in oracle without
>>> get_function() column and do a bulk insert from Spark dataframe to that
>>> staging table?
>>>
>>> HTH
>>>
>>> Mich
>>>
>>>
>>>
>>>
>>> view my Linkedin profile
>>> <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>
>>>
>>>
>>>
>>> *Disclaimer:* Use it at your own risk. Any and all responsibility for
>>> 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 Fri, 18 Jun 2021 at 21:53, Anshul Kala <anshul.k...@gmail.com> wrote:
>>>
>>>>
>>>> Hi Mich,
>>>>
>>>> Thanks for your reply. Please advise the insert query that I need to
>>>> substitute should be like below:
>>>>
>>>> Insert into table(a,b,c) values(?,get_function_value(?),?)
>>>>
>>>> In the statement above :
>>>>
>>>> ? : refers to value from dataframe column values
>>>> get_function_value : refers to be the function where one of the data
>>>> frame column is passed as input
>>>>
>>>>
>>>> Thanks
>>>> Anshul
>>>>
>>>>
>>>> Thanks
>>>> Anshul
>>>>
>>>> On Fri, Jun 18, 2021 at 4:29 PM Mich Talebzadeh <
>>>> mich.talebza...@gmail.com> wrote:
>>>>
>>>>> I gather you mean using JDBC to write to the Oracle table?
>>>>>
>>>>> Spark provides a unified framework to write to any JDBC
>>>>> compliant database.
>>>>>
>>>>> def writeTableWithJDBC(dataFrame, url, tableName, user, password,
>>>>> driver, mode):
>>>>> try:
>>>>> dataFrame. \
>>>>> write. \
>>>>> format("jdbc"). \
>>>>> option("url", url). \
>>>>> option("dbtable", tableName). \
>>>>> option("user", user). \
>>>>> option("password", password). \
>>>>> option("driver", driver). \
>>>>> mode(mode). \
>>>>> save()
>>>>> except Exception as e:
>>>>> print(f"""{e}, quitting""")
>>>>> sys.exit(1)
>>>>>
>>>>> and how to write it
>>>>>
>>>>> def loadIntoOracleTable(self, df2):
>>>>> # write to Oracle table, all uppercase not mixed case and
>>>>> column names <= 30 characters in version 12.1
>>>>> tableName =
>>>>> self.config['OracleVariables']['yearlyAveragePricesAllTable']
>>>>> fullyQualifiedTableName =
>>>>> self.config['OracleVariables']['dbschema']+'.'+tableName
>>>>> user = self.config['OracleVariables']['oracle_user']
>>>>> password = self.config['OracleVariables']['oracle_password']
>>>>> driver = self.config['OracleVariables']['oracle_driver']
>>>>> mode = self.config['OracleVariables']['mode']
>>>>>
>>>>> s.writeTableWithJDBC(df2,oracle_url,fullyQualifiedTableName,user,password,driver,mode)
>>>>> print(f"""created
>>>>> {config['OracleVariables']['yearlyAveragePricesAllTable']}""")
>>>>> # read data to ensure all loaded OK
>>>>> fetchsize = self.config['OracleVariables']['fetchsize']
>>>>> read_df =
>>>>> s.loadTableFromJDBC(self.spark,oracle_url,fullyQualifiedTableName,user,password,driver,fetchsize)
>>>>> # check that all rows are there
>>>>> if df2.subtract(read_df).count() == 0:
>>>>> print("Data has been loaded OK to Oracle table")
>>>>> else:
>>>>> print("Data could not be loaded to Oracle table, quitting")
>>>>> sys.exit(1)
>>>>>
>>>>> in the statement where it says
>>>>>
>>>>> option("dbtable", tableName). \
>>>>>
>>>>> You can replace *tableName* with the equivalent SQL insert statement
>>>>>
>>>>> You will need JDBC driver for Oracle say ojdbc6.jar in
>>>>> $SPARK_HOME/conf/spark-defaults.conf
>>>>>
>>>>> spark.driver.extraClassPath
>>>>> /home/hduser/jars/jconn4.jar:/home/hduser/jars/ojdbc6.jar
>>>>>
>>>>> HTH
>>>>>
>>>>>
>>>>>
>>>>> view my Linkedin profile
>>>>> <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>
>>>>>
>>>>>
>>>>>
>>>>> *Disclaimer:* Use it at your own risk. Any and all responsibility for
>>>>> 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 Fri, 18 Jun 2021 at 20:49, Anshul Kala <anshul.k...@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> Hi All,
>>>>>>
>>>>>> I am using spark to ingest data from file to database Oracle table .
>>>>>> For one of the fields , the value to be populated is generated from a
>>>>>> function that is written in database .
>>>>>>
>>>>>> The input to the function is one of the fields of data frame
>>>>>>
>>>>>> I wanted to use spark.dbc.write to perform the operation, which
>>>>>> generates the insert query at back end .
>>>>>>
>>>>>> For example : It can generate the insert query as :
>>>>>>
>>>>>> Insert into table values (?,?, getfunctionvalue(?) )
>>>>>>
>>>>>> Please advise if it is possible in spark and if yes , how can it be
>>>>>> done
>>>>>>
>>>>>> This is little urgent for me . So any help is appreciated
>>>>>>
>>>>>> Thanks
>>>>>> Anshul
>>>>>>
>>>>>
