We have a hive metastore in mysql db. Use case is to query all the updates alters or deletes done to the hive metadata from past one month. We need to query this information to update an existing data catalog about this metadata.
We need only the delta changes happened from past query pull. How to query changes from hive metastore tables to check changes happened on a metadata during a time interval?
Tried few query sample to pull metadata.
We need to query this information to update an existing data catalog about this metadata.
We need only the delta changes happened from past query pull. How to query changes from hive metastore tables to check changes happened on a metadata during a time interval?
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I have a daily ingestion of data into HDFS . From data into HDFS I generate Hive tables partitioned by date and another column. One day has 130G data. After generate the data, I run msck repair. Now every msck tasks more than 2 hours. In my mind, msck will scan the whole table data (we have about 200 days data) and then update metadata. My question is: is there a way let the msck only scan the last day data and then update the metadata to speed up the whole process? by the way there is no small files issue, I already merge the small files before msck.
When you creating external table or doing repair/recover partitions with this configuration:
set hive.stats.autogather=true;
Hive scans each file in the table location to get statistics and it can take too much time.
The solution is to switch it off before create/alter table/recover partitions
set hive.stats.autogather=false;
See these related tickets: HIVE-18743, HIVE-19489, HIVE-17478
If you need statistics, you can gather statistics only for new partitions if necessary using
ANALYZE TABLE [db_name.]tablename [PARTITION(partcol1[=val1], partcol2[=val2], ...)]
COMPUTE STATISTICS
See details here: ANALYZE TABLE
Also if you know which partitions should be added, use ALTER TABLE ADD PARTITION - you can add many partitions in single command.
I am streaming data into BQ, every day I run a scheduled job in Dataprep that takes 24 hours of data and modifies some data and creates a new table in the BQ dataset with 24 hours of data.
The original table though stays unmodified and keeps on gathering data.
What I would like to do is delete all rows in the table after the dataprep makes a copy so that a new 24 hours of data streaming is gathered
How can I make this automated, I can't seem to find anything in dataprep that drops the original table and creating a new table.
You can do this setting up your table as partitioned table due to you are ingesting data constantly.
This option is to do it manually:
bq rm '[YOUR_DATASET].[YOUR_TABLE]$xxxxxxx'
And with the expiration time you can set the time when the data of the table will be deleted:
bq update --time_partitioning_expiration [INTEGER] [YOUR_PROJECT_ID]:[YOUR_DATASET].[YOUR_TABLE]
You could use a Scheduled Query to clear out the table:
https://cloud.google.com/bigquery/docs/scheduling-queries
Scheduled queries support DDL so you could schedule a query that deletes all the rows from this table, or deletes the table entirely, on a daily basis. at a specific time.
We have an Oracle Database which has about 500 000 records and we have an etl job which runs every day and it will delete all the records from Oracle and reinsert the data (because some of the data is updated on every day) and in this process the lastupdatedTime for all the records is current day. This whole process is maintained by some other team.
And We have an application which is related to search, for this we are using Solr for the better performance. To use Solr, we are reading all the data from Oracle DB and storing into SolrCollection and this is a separate Job.
My questions, is there any better way to get only the modified records from Oracle and update only those into my Solr Collection. If we go with lastUpdatedTime in Oracle Db it wont help in my scenario.
I am trying to have a setup in Pentaho where :
My source data is in MySQL DB and target database is Amazon redshift.
I want to have incremental loads on Redshift database table, based on the last updated timestamp from MySQL DB table.
Primary key is student ID.
Can I implement this using update/insert in Pentaho ?
Insert/Update step in Pentaho Data Integration serves the purpose of inserting the row if it doesn't exist in the destination table or updating it if it's already there. It has nothing to do with incremental loads, but if your loads should be inserting or updating the record based on some Change Data Capture mechanism then this is the right step at the end of the process.
For example you could go one of two ways:
If you have a CDC then limit the data at Table Input for MySQL since you already know the last time a record has been modified (last load)
If you don't have a CDC and you are comparing entire tables then go for joining the sets to produce rows that has changed and then perform a load (slower solution)
I have a huge schema containing billions of records, I want to purge data older than 13 months from it and maintain it as a backup in such a way that it can be recovered again whenever required.
Which is the best way to do it in SQL - can we create a separate copy of this schema and add a delete trigger on all tables so that when trigger fires, purged data gets inserted to this new schema?
Will there be only one record per delete statement if we use triggers? Or all records will be inserted?
Can we somehow use bulk copy?
I would suggest this is a perfect use case for the Stretch Database feature in SQL Server 2016.
More info: https://msdn.microsoft.com/en-gb/library/dn935011.aspx
The cold data can be moved to the cloud with your given date criteria without any applications or users being aware of it when querying the database. No backups required and very easy to setup.
There is no need for triggers, you can use job running every day, that will put outdated data into archive tables.
The best way I guess is to create a copy of current schema. In main part - delete all that is older then 13 months, in archive part - delete all for last 13 month.
Than create SP (or any SPs) that will collect data - put it into archive and delete it from main table. Put this is into daily running job.
The cleanest and fastest way to do this (with billions of rows) is to create a partitioned table probably based on a date column by month. Moving data in a given partition is a meta operation and is extremely fast (if the partition setup and its function is set up properly.) I have managed 300GB tables using partitioning and it has been very effective. Be careful with the partition function so dates at each edge are handled correctly.
Some of the other proposed solutions involve deleting millions of rows which could take a long, long time to execute. Model the different solutions using profiler and/or extended events to see which is the most efficient.
I agree with the above to not create a trigger. Triggers fire with every insert/update/delete making them very slow.
You may be best served with a data archive stored procedure.
Consider using multiple databases. The current database that has your current data. Then an archive or multiple archive databases where you move your records out from your current database to with some sort of say nightly or monthly stored procedure process that moves the data over.
You can use the exact same schema as your production system.
If the data is already in the database no need for a Bulk Copy. From there you can backup your archive database so it is off the sql server. Restore the database if needed to make the data available again. This is much faster and more manageable than bulk copy.
According to Microsoft's documentation on Stretch DB (found here - https://learn.microsoft.com/en-us/azure/sql-server-stretch-database/), you can't update or delete rows that have been migrated to cold storage or rows that are eligible for migration.
So while Stretch DB does look like a capable technology for archive, the implementation in SQL 2016 does not appear to support archive and purge.