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From Matt Burgess <mattyb...@apache.org>
Subject Re: Question about NiFi and bulk database inserts - is PutSQL the only out of the box option?
Date Thu, 09 Aug 2018 15:36:06 GMT
Boris,

That is correct, PutDatabaseRecord uses PreparedStatements and statement
batches according to the JDBC spec, rather than proprietary or
vendor-specific solutions. The latter is very difficult to support and
maintain; some support bulk insert SQL commands, some have external
programs (each with their own syntax and behavior), etc.

For the case where there is a better bulk insert option using a SQL
dialect, we could consider a SQLRecordSetWriter. This could be leveraged by
ConvertRecord to basically replace ConvertJSONToSQL, and it could ask the
database adapter for dialect-specific SQL for bulk insert. Then if any
additional changes need to be made to the SQL statements, you still have a
chance to change them before sending to PutSQL.  However, if the database
does not support a bulk insert option via SQL, then the SQLWriter could
degrade to worse performance than the older ConvertJSONToSQL -> PutSQL
option, since you wouldn't be able to leverage the attributes for values
and types to be used in a PreparedStatement; they would just be a bunch of
full INSERT lines in a flow file. I think that tradeoff makes the
record-based SQL solution less attractive (and indeed, is what has kept me
from working on a SQLWriter thus far).

Per your comment about dumping the data somewhere like HDFS, I agree there
are definitely situations in which you would rather put a lot of data in
one place and run a bulk ingest tool, rather than having the data flow
through NiFi. Often this happens when the interface to the target system
does not support efficient ingest (such as INSERT statements in SQL).
Before the Hive 3 bundle (the Hive Streaming API is supposed to be much
faster/better than before), I would often put the data in HDFS and then
create a table atop it using PutHiveQL. In fact, that's why I added a
partial CREATE TABLE IF NOT EXISTS statement to the ConvertAvroToORC
processor, so you could dump the ORC file to HDFS, then use ReplaceText to
fill the content with the DDL statement, then send that to PutHiveQL.
Other folks even leave the conversion to Hive, by setting up a raw external
table by dumping the data, then doing a INSERT FROM SELECT at Hive to pull
the data from the raw external table and convert it to a different format
for the managed target table.

I'm always interested in performance improvements we can make, especially
in the RDBMS world, so I'm all ears for ideas :)

Regards,
Matt





On Thu, Aug 9, 2018 at 11:22 AM Boris Tyukin <boris@boristyukin.com> wrote:

> Matt, but it still not using bulk load methods, right?. Some databases
> have proprietary ways of doing that fast rather than running a bunch of
> insert statements.
>
> Bob, what I've done in the past is dumping data either to HDFS or local
> disk and then using efficient tools to do this job using bulk load tools,
> specific to your target platform. Sqoop can also do it to some degree but
> has a long list of limitations then it comes down to exports.
>
>
> On Thu, Aug 9, 2018 at 11:03 AM Matt Burgess <mattyb149@apache.org> wrote:
>
>> Bob,
>>
>> Unless you already have SQL in your flow files, I always recommend
>> PutDatabaseRecord [1] over PutSQL. The former is basically a mashup of
>> ConvertJSONToSQL -> PutSQL, but takes in data in any format supported by
>> our record readers (CSV, Avro, XML, JSON, etc.) and takes care of all the
>> SQL generation (and prepared statement stuff) under the hood. You should
>> find it a lot easier to work with, and a lot faster than the older
>> alternative, especially since PutDatabaseRecord is able to deal with an
>> entire set of rows/records in one flow file, rather than having to split up
>> large CSV files, e.g. into individual rows to get individual SQL
>> statements. If you try it out, please let us know if you run into any
>> issues, I will do my best to help get you up and going.
>>
>> Regards,
>> Matt
>>
>> [1]
>> https://nifi.apache.org/docs/nifi-docs/components/org.apache.nifi/nifi-standard-nar/1.7.1/org.apache.nifi.processors.standard.PutDatabaseRecord/index.html
>>
>>
>> On Thu, Aug 9, 2018 at 10:52 AM Kuhfahl, Bob <rkuhfahl@mitre.org> wrote:
>>
>>> I’m trying to get bulk inserts going using PutSQL processor but it’s
>>> starting to get ugly so I need to reach out and see if any of you have been
>>> down this path.
>>>
>>>
>>>
>>> If you have, here’s some info.  If not, thanks for reading this far ☺
>>>
>>>
>>>
>>> Background:
>>>
>>> Legacy database migration ETL task.  Extract from one database, do a
>>> bunch of transformations, then load it all into a postgresql repo.
>>>
>>> We have 100’s of tables with obviously many record structures *_and a
>>> ton of data_.*
>>>
>>>
>>>
>>> According to:
>>>
>>>
>>> https://community.hortonworks.com/articles/91849/design-nifi-flow-for-using-putsql-processor-to-per.html
>>>
>>>
>>>
>>> PutSQL, to do batch inserts, seems to want the form of the SQL statement
>>> to be identical for each record type.
>>>
>>> e.g. Insert into Employee ("name", "job title") VALUES (?,?)
>>>
>>>
>>>
>>> Easy enough to build that *but* then it needs attributes for all the
>>> values and types in the flow.
>>>
>>> e.g.
>>>
>>> 1.  sql.args.1.value = Bryan B
>>>
>>> 2.  sql.args.2.value = Director
>>>
>>> Use Update Attribute Processor to set sql.args.N.type Flow file
>>> attributes
>>>
>>> 1.  sql.args.1.type = 12 (VARCHAR)
>>>
>>> 2.  sql.args.2.type = 12
>>>
>>>
>>>
>>> THIS implies my flow will need to create a couple attributes for every
>>> single field in the dataflow – AND I’ll have to come up with logic to
>>> determine what the data type is…
>>>
>>>
>>>
>>> I’m a newbie at this nifi stuff but that really does _not_ feel like I’m
>>> going down a good path.
>>>
>>> I’m hand-jamming a proof of concept just to validate the above, but
>>> having a hard time lining up the data types… (e.g. the database has a
>>> char(2) field; trying char, trying varchar, …)
>>>
>>>
>>>
>>> The other SQL “insert-able” processors seem to want to read a file
>>> instead of a flow, but I could easily be missing something.
>>>
>>> Suggestions would be appreciated!
>>>
>>>
>>>
>>

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