I need to process millions of records coming from MongoDb and put a ETL pipeline to insert that data into a PostgreSQL database. However, in all the methods I've tried, I keep getting the out memory heap space exception. Here's what I've already tried -
Tried connecting to MongoDB using tMongoDBInput and put a tMap to process the records and output them using a connection to PostgreSQL. tMap could not handle it.
Tried to load the data into a JSON file and then read from the file to PostgreSQL. Data got loaded into JSON file but from there on got the same memory exception.
Tried increasing the RAM for the job in the settings and tried the above two methods again, still no change.
I specifically wanted to know if there's any way to stream this data or process it in batches to counter the memory issue.
Also, I know that there are some components dealing with BulkDataLoad. Could anyone please confirm whether it would be helpful here since I want to process the records before inserting and if yes, point me to the right kind of documentation to get that set up.
Thanks in advance!
As you already tried all the possibilities the only way that I can see to do this requirement is breaking done the job into multiple sub-jobs or going with incremental load based on key columns or date columns, Considering this as a one-time activity for now.
Please let me know if it helps.
Related
I am running into an issue where I have a set up a pipeline that gets a list of tables from Teradata using a Lookup activity and then passes those items to a ForEach activity that then copies the data in parallel and saves them as a gzipped file. The requirement is to essentially archive some tables that are no longer being used.
For this pipeline I am not using any partition options as most of the tables are small and I kept it to be flexible.
Pipeline
COPY activity within ForEach activity
99% of the tables ran without issues and were copied as gz files into blob storage, but two tables in particular run for long time (apprx 4 to 6 hours) without any of the data being written into a blob storage account.
Note that the image above says "Cancelled", but that was done by me. Before that I had a run time as described above, but still no data being written. This is affecting only 2 tables.
I checked with our Teradata team and those tables are not being used by any one (hence its not locked). I also looked at "Teradata Viewpoint" (admin tool) and looked at the query monitor and saw that the query was running on Teradata without issues.
Any insight would be greatly apreciated.
Onlooking issue mention it look the data size of table is more than a blob can store ( As you are not using any partition options )
Use partition option for optimize performance and hold the data
Link
Just in case someone else comes across this, the way I solved this was to create a new data store connection called "TD_Prod_datasetname". The purpose of this dataset is to not point to a specific table, but to just accept a "item().TableName" value.
This datasource contains two main values. 1st is the #dataset().TeradataName
Dataset property
I only came up with that after doing a little bit of digging in Google.
I then created a parameter called "TeradataTable" as String.
I then updated my pipeline. As above the main two activities remain the same. I have a lookup and then a ForEach Activity (where for each will get the item values):
However, in the COPY command inside the ForEach activity I updated the source. Instead of getting "item().Name" I am passing through #item().TableName:
This then enabled me to then select the "Table" option and because I am using Table instead of query I can then use the "Hash" partition. I left it blank because according to Microsoft documentation it will automatically find the Primary Key that will be used for this.
The only issue that I ran into when using this was that if you run into a table that does not have a Primary Key then this item will fail and will need to be run through either a different process or manually outside of this job.
Because of this change the previously files that just hung there and did not copy now copied successfully into our blob storage account.
Hope this helps someone else that wants to see how to create parallel copies using Teradata as a source and pass through multiple table values.
It's my first Druid experience.
I have got a local setup of Druid in local machine.
Now I'd like to make some query performance test. My test data is a huge local json file 1.2G.
The idea was to load it into druid and run required SQL query. The file is getting parsed and successfully processed (I'm using Druid web-based UI to submit an injection task).
The problem I run into is the datasource size. It doesn't makes sense that 1.2G of raw json data results in 35M of datasource. Is there any limitation the locally running Druid setup has. I think the test data is processed partially. Unfortunately didn't find any relevant config to change it. Will appreciate if some one is able to shed light on this.
Thanks in advance
With druid 80-90 percent compression is expected. I have seen 2GB CSV file reduced to 200MB druid datasoruce.
Can you query the count to make sure all data is ingested? All please disable approximate algorithm hyper-log-log to get exact count.Druid SQL will switch to exact distinct counts if you set "useApproximateCountDistinct" to "false", either through query context or through broker configuration.( refer http://druid.io/docs/latest/querying/sql.html )
Also can check logs for exception and error messages. If it faces problem to ingest particular JSON record it skips that record.
well my problem is, how could i copy a database with talend from postgresql to sap hana without needing to write a job for every table ?
The reason for this is, because it could take some long time to prepare all those jobs, while taking in consideration, having at least 200 tables, which at least have 30 columns.
I tried tTransferDatabase plugin, but i can't success to transfer it to sap hana, it gives me an error that it can't copy schema (while it successfully worked copying it to other database in postgresql), and i am sure that the schemas names are right.
here is the error:
Exception in component tTransferDatabase_1
java.lang.NullPointerException
at org.apache.ddlutils.PlatformFactory.createNewPlatformInstance(PlatformFactory.java:86)
at org.apache.ddlutils.PlatformFactory.createNewPlatformInstance(PlatformFactory.java:124)
at com.devjpcb.transferdatabase.TransferDatabase.getPlatformDestine(TransferDatabase.java:179)
at com.devjpcb.transferdatabase.TransferDatabase.copySchemaToDatabase(TransferDatabase.java:249)
at local_project.aaasa_0_1.aaasa.tTransferDatabase_1Process(aaasa.java:836)
at local_project.aaasa_0_1.aaasa.runJobInTOS(aaasa.java:1130)
at local_project.aaasa_0_1.aaasa.main(aaasa.java:951)
Is there maybe a chance to do sth like .. for each table in connection, table guess schema, copy columns from table to other side of tmap, run ?
Any advice would be helpful ;), Thank you !
With some work, you could use the example job created by rbaldwin on Talend Exchange; note that it starts with files, not a database. But you could easily create a job that loops through all your database tables and does an extract to file, to then use as the starting point.
Another option is Bekwam's solution
We have been using BigQuery for over a year now with no issues. We load data as batch jobs every few hours and it usually is instantly available.
We just started experimenting with streaming inserts using template tables. With our first test, we saw no errors and the data showed up instantly. The test created approximately 120 tables. A simple select count (using the web ui) on the tables came up with the right total number of ~8000 rows. After a couple of hours of more streaming, the total dropped to ~1400 rows.
Unsure about what happened, we dropped the dataset, recreated the template table and re-ran the streaming. This time around, the tables showed up right away but the data did not. On our third attempt the tables themselves did not show up for more than a couple of hours. We are on the fourth attempt and this time we only streamed data belonging to one table. The table showed up right away, but it has been over an hour and the data does not show up.
The streaming service uses the latest Java library, inserts only one record at a time and logs the response. The response, without an exception is always {"kind":"bigquery#tableDataInsertAllResponse"} and no errors.
Any help trying to understand what is happening would be great. Thanks.
Looks like we've identified the issue. It appears there's a race in the template-tables path only that causes our system to think the first chunk of data was deleted by user action (table truncation -- which it obviously wasn't), and is dropped. We've identified the fix and will attempt to push out a fix shortly.
Thanks for letting us know!
In the spring batch program, I am reading the records from a file and comparing with the DB if the data say column1 from file is already exists in table1.
Table1 is fairly small and static. Is there a way I can get all the data from table1 and store it in memory in the spring batch code? Right now for every record in the file, the select query is hitting the DB.
The file is having 3 columns delimited with "|".
The file I am reading is having on an average 12 million records and it is taking around 5 hours to complete the job.
Preload in memory using a StepExecutionListener.beforeStep (or #BeforeStep).
Using this trick data will be loaded once before step execution.
This also works for step restarting.
I'd use caching like a standard web app. Add service caching using Spring's caching abstractions and that should take care of it IMHO.
Load static table in JobExecutionListener.beforeJob(-) and keep this in jobContext and you can access through multiple steps using 'Late Binding of Job and Step Attributes'.
You may refer 5.4 section of this link http://docs.spring.io/spring-batch/reference/html/configureStep.html