Another option: https://github.com/mysql-time-machine/replicator
>From the readme:
"Replicates data changes from MySQL binlog to HBase or Kafka. In case of
HBase, preserves the previous data versions. HBase storage is intended for
auditing purposes of historical data. In addition, special daily-changes
tables can be maintained in HBase, which are convenient for fast and cheap
imports from HBase to Hive. Replication to Kafka is intended for easy
real-time access to a stream of data changes."
On Tue, Jan 3, 2017 at 10:39 PM, Yuanzhe Yang <yyz1...@gmail.com> wrote:
> Hi Ayan,
>
> This "inline view" idea is really awesome and enlightens me! Finally I
> have a plan to move on. I greatly appreciate your help!
>
> Best regards,
> Yang
>
> 2017-01-03 18:14 GMT+01:00 ayan guha <guha.a...@gmail.com>:
>
>> Ahh I see what you mean....I confused two terminologies....because we
>> were talking about partitioning and then changed topic to identify changed
>> data ....
>>
>> For that, you can "construct" a dbtable as an inline view -
>>
>> viewSQL = "(select * from table where <column> >
>> '<last_modified_value>')".replace("<column>","inserted_on").
>> replace("<last_modified_value>",checkPointedValue)
>> dbtable =viewSQL
>>
>> refer to this
>> <http://www.sparkexpert.com/2015/03/28/loading-database-data-into-spark-using-data-sources-api/>
>> blog...
>>
>> So, in summary, you have 2 things
>>
>> 1. Identify changed data - my suggestion to use dbtable with inline view
>> 2. parallelism - use numPartition,lowerbound,upper bound to generate
>> number of partitions
>>
>> HTH....
>>
>>
>>
>> On Wed, Jan 4, 2017 at 3:46 AM, Yuanzhe Yang <yyz1...@gmail.com> wrote:
>>
>>> Hi Ayan,
>>>
>>> Yeah, I understand your proposal, but according to here
>>> http://spark.apache.org/docs/latest/sql-programming-gui
>>> de.html#jdbc-to-other-databases, it says
>>>
>>> Notice that lowerBound and upperBound are just used to decide the
>>> partition stride, not for filtering the rows in table. So all rows in the
>>> table will be partitioned and returned. This option applies only to reading.
>>>
>>> So my interpretation is all rows in the table are ingested, and this
>>> "lowerBound" and "upperBound" is the span of each partition. Well, I am not
>>> a native English speaker, maybe it means differently?
>>>
>>> Best regards,
>>> Yang
>>>
>>> 2017-01-03 17:23 GMT+01:00 ayan guha <guha.a...@gmail.com>:
>>>
>>>> Hi
>>>>
>>>> You need to store and capture the Max of the column you intend to use
>>>> for identifying new records (Ex: INSERTED_ON) after every successful run of
>>>> your job. Then, use the value in lowerBound option.
>>>>
>>>> Essentially, you want to create a query like
>>>>
>>>> select * from table where INSERTED_ON > lowerBound and
>>>> INSERTED_ON<upperBound
>>>>
>>>> everytime you run the job....
>>>>
>>>>
>>>>
>>>> On Wed, Jan 4, 2017 at 2:13 AM, Yuanzhe Yang <yyz1...@gmail.com> wrote:
>>>>
>>>>> Hi Ayan,
>>>>>
>>>>> Thanks a lot for your suggestion. I am currently looking into sqoop.
>>>>>
>>>>> Concerning your suggestion for Spark, it is indeed parallelized with
>>>>> multiple workers, but the job is one-off and cannot keep streaming.
>>>>> Moreover, I cannot specify any "start row" in the job, it will always
>>>>> ingest the entire table. So I also cannot simulate a streaming process by
>>>>> starting the job in fix intervals...
>>>>>
>>>>> Best regards,
>>>>> Yang
>>>>>
>>>>> 2017-01-03 15:06 GMT+01:00 ayan guha <guha.a...@gmail.com>:
>>>>>
>>>>>> Hi
>>>>>>
>>>>>> While the solutions provided by others looks promising and I'd like
>>>>>> to try out few of them, our old pal sqoop already "does" the job. It has
>>>>>> a
>>>>>> incremental mode where you can provide a --check-column and
>>>>>> --last-modified-value combination to grab the data - and yes, sqoop
>>>>>> essentially does it by running a MAP-only job which spawns number of
>>>>>> parallel map task to grab data from DB.
>>>>>>
>>>>>> In Spark, you can use sqlContext.load function for JDBC and use
>>>>>> partitionColumn and numPartition to define parallelism of connection.
>>>>>>
>>>>>> Best
>>>>>> Ayan
>>>>>>
>>>>>> On Tue, Jan 3, 2017 at 10:49 PM, Yuanzhe Yang <yyz1...@gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>>> Hi Ayan,
>>>>>>>
>>>>>>> Thanks a lot for such a detailed response. I really appreciate it!
>>>>>>>
>>>>>>> I think this use case can be generalized, because the data is
>>>>>>> immutable and append-only. We only need to find one column or timestamp
>>>>>>> to
>>>>>>> track the last row consumed in the previous ingestion. This pattern
>>>>>>> should
>>>>>>> be common when storing sensor data. If the data is mutable, then the
>>>>>>> solution will be surely difficult and vendor specific as you said.
>>>>>>>
>>>>>>> The workflow you proposed is very useful. The difficulty part is how
>>>>>>> to parallelize the ingestion task. With Spark when I have multiple
>>>>>>> workers
>>>>>>> working on the same job, I don't know if there is a way and how to
>>>>>>> dynamically change the row range each worker should process in
>>>>>>> realtime...
>>>>>>>
>>>>>>> I tried to find out if there is any candidate available out of the
>>>>>>> box, instead of reinventing the wheel. At this moment I have not
>>>>>>> discovered
>>>>>>> any existing tool can parallelize ingestion tasks on one database. Is
>>>>>>> Sqoop
>>>>>>> a proper candidate from your knowledge?
>>>>>>>
>>>>>>> Thank you again and have a nice day.
>>>>>>>
>>>>>>> Best regards,
>>>>>>> Yang
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> 2016-12-30 8:28 GMT+01:00 ayan guha <guha.a...@gmail.com>:
>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>> "If data ingestion speed is faster than data production speed,
>>>>>>>> then eventually the entire database will be harvested and those workers
>>>>>>>> will start to "tail" the database for new data streams and the
>>>>>>>> processing
>>>>>>>> becomes real time."
>>>>>>>>
>>>>>>>> This part is really database dependent. So it will be hard to
>>>>>>>> generalize it. For example, say you have a batch interval of 10
>>>>>>>> secs....what happens if you get more than one updates on the same row
>>>>>>>> within 10 secs? You will get a snapshot of every 10 secs. Now,
>>>>>>>> different
>>>>>>>> databases provide different mechanisms to expose all DML changes,
>>>>>>>> MySQL has
>>>>>>>> binlogs, oracle has log shipping, cdc,golden gate and so
>>>>>>>> on....typically it
>>>>>>>> requires new product or new licenses and most likely new component
>>>>>>>> installation on production db :)
>>>>>>>>
>>>>>>>> So, if we keep real CDC solutions out of scope, a simple snapshot
>>>>>>>> solution can be achieved fairly easily by
>>>>>>>>
>>>>>>>> 1. Adding INSERTED_ON and UPDATED_ON columns on the source
>>>>>>>> table(s).
>>>>>>>> 2. Keeping a simple table level check pointing (TABLENAME,TS_MAX)
>>>>>>>> 3. Running an extraction/load mechanism which will take data from
>>>>>>>> DB (where INSERTED_ON > TS_MAX or UPDATED_ON>TS_MAX) and put to HDFS.
>>>>>>>> This
>>>>>>>> can be sqoop,spark,ETL tool like informatica,ODI,SAP etc. In addition,
>>>>>>>> you
>>>>>>>> can directly write to Kafka as well. Sqoop, Spark supports Kafka. Most
>>>>>>>> of
>>>>>>>> the ETL tools would too...
>>>>>>>> 4. Finally, update check point...
>>>>>>>>
>>>>>>>> You may "determine" checkpoint from the data you already have in
>>>>>>>> HDFS if you create a Hive structure on it.
>>>>>>>>
>>>>>>>> Best
>>>>>>>> AYan
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>> On Fri, Dec 30, 2016 at 4:45 PM, 任弘迪 <ryan.hd....@gmail.com> wrote:
>>>>>>>>
>>>>>>>>> why not sync binlog of mysql(hopefully the data is immutable and
>>>>>>>>> the table is append-only), send the log through kafka and then
>>>>>>>>> consume it
>>>>>>>>> by spark streaming?
>>>>>>>>>
>>>>>>>>> On Fri, Dec 30, 2016 at 9:01 AM, Michael Armbrust <
>>>>>>>>> mich...@databricks.com> wrote:
>>>>>>>>>
>>>>>>>>>> We don't support this yet, but I've opened this JIRA as it sounds
>>>>>>>>>> generally useful: https://issues.apache.
>>>>>>>>>> org/jira/browse/SPARK-19031
>>>>>>>>>>
>>>>>>>>>> In the mean time you could try implementing your own Source, but
>>>>>>>>>> that is pretty low level and is not yet a stable API.
>>>>>>>>>>
>>>>>>>>>> On Thu, Dec 29, 2016 at 4:05 AM, "Yuanzhe Yang (杨远哲)" <
>>>>>>>>>> yyz1...@gmail.com> wrote:
>>>>>>>>>>
>>>>>>>>>>> Hi all,
>>>>>>>>>>>
>>>>>>>>>>> Thanks a lot for your contributions to bring us new technologies.
>>>>>>>>>>>
>>>>>>>>>>> I don't want to waste your time, so before I write to you, I
>>>>>>>>>>> googled, checked stackoverflow and mailing list archive with
>>>>>>>>>>> keywords
>>>>>>>>>>> "streaming" and "jdbc". But I was not able to get any solution to
>>>>>>>>>>> my use
>>>>>>>>>>> case. I hope I can get some clarification from you.
>>>>>>>>>>>
>>>>>>>>>>> The use case is quite straightforward, I need to harvest a
>>>>>>>>>>> relational database via jdbc, do something with data, and store
>>>>>>>>>>> result into
>>>>>>>>>>> Kafka. I am stuck at the first step, and the difficulty is as
>>>>>>>>>>> follows:
>>>>>>>>>>>
>>>>>>>>>>> 1. The database is too large to ingest with one thread.
>>>>>>>>>>> 2. The database is dynamic and time series data comes in
>>>>>>>>>>> constantly.
>>>>>>>>>>>
>>>>>>>>>>> Then an ideal workflow is that multiple workers process
>>>>>>>>>>> partitions of data incrementally according to a time window. For
>>>>>>>>>>> example,
>>>>>>>>>>> the processing starts from the earliest data with each batch
>>>>>>>>>>> containing
>>>>>>>>>>> data for one hour. If data ingestion speed is faster than data
>>>>>>>>>>> production
>>>>>>>>>>> speed, then eventually the entire database will be harvested and
>>>>>>>>>>> those
>>>>>>>>>>> workers will start to "tail" the database for new data streams and
>>>>>>>>>>> the
>>>>>>>>>>> processing becomes real time.
>>>>>>>>>>>
>>>>>>>>>>> With Spark SQL I can ingest data from a JDBC source with
>>>>>>>>>>> partitions divided by time windows, but how can I dynamically
>>>>>>>>>>> increment the
>>>>>>>>>>> time windows during execution? Assume that there are two workers
>>>>>>>>>>> ingesting
>>>>>>>>>>> data of 2017-01-01 and 2017-01-02, the one which finishes quicker
>>>>>>>>>>> gets next
>>>>>>>>>>> task for 2017-01-03. But I am not able to find out how to increment
>>>>>>>>>>> those
>>>>>>>>>>> values during execution.
>>>>>>>>>>>
>>>>>>>>>>> Then I looked into Structured Streaming. It looks much more
>>>>>>>>>>> promising because window operations based on event time are
>>>>>>>>>>> considered
>>>>>>>>>>> during streaming, which could be the solution to my use case.
>>>>>>>>>>> However, from
>>>>>>>>>>> documentation and code example I did not find anything related to
>>>>>>>>>>> streaming
>>>>>>>>>>> data from a growing database. Is there anything I can read to
>>>>>>>>>>> achieve my
>>>>>>>>>>> goal?
>>>>>>>>>>>
>>>>>>>>>>> Any suggestion is highly appreciated. Thank you very much and
>>>>>>>>>>> have a nice day.
>>>>>>>>>>>
>>>>>>>>>>> Best regards,
>>>>>>>>>>> Yang
>>>>>>>>>>> ------------------------------------------------------------
>>>>>>>>>>> ---------
>>>>>>>>>>> To unsubscribe e-mail: user-unsubscr...@spark.apache.org
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>> --
>>>>>>>> Best Regards,
>>>>>>>> Ayan Guha
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>> --
>>>>>> Best Regards,
>>>>>> Ayan Guha
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>> --
>>>> Best Regards,
>>>> Ayan Guha
>>>>
>>>
>>>
>>
>>
>> --
>> Best Regards,
>> Ayan Guha
>>
>
>
--
Ben Teeuwen
Senior Data Scientist
Booking.com B.V.
Rembrandtplein 29-45 Amsterdam 1017CT Netherlands
Direct +31207153058
[image: Booking.com] <http://www.booking.com/>
The world's #1 accommodation site
43 languages, 187+ offices worldwide, 96,000+ global destinations,
1,200,000+ room nights booked every day
No booking fees, best price always guaranteed
Subsidiary of the Priceline Group (NASDAQ: PCLN)
