I have a schema specific for temporary tables in redshift. Eventually, as creation of a lot of tables takes a lot of space, I would like to know the following:
Is there a way to automate deletion of tables in that schema after X days(lets say 30 days) after the table's creation date?
Any articles on the above question I can refer to?
Thanks.
You could start on Is there any way to find table creation date in redshift?
You can first collect the output to a temporary table and then run something that DROPs tables that have age over your threshold or you can do it in one step.
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I have two databases - Cloudant and IBM Db2. I have a table in each of these databases that hold static data that is only read from and never updated. These were created a long time ago and I'm not sure if they are used today so I wish to do a clean-up.
I want to determine if these tables or rows from these tables, are still being read from.
Is there a way to record the read timestamp (or at least know if it is simply accessed like a dirty bit) on a row of the table when it is read from?
OR
Record the read timestamp of the entire table (if any record from it is accessed)?
There is SYSCAT.TABLES.LASTUSED system catalog column in Db2 for DML statements on whole table.
There is no way to track each table row read access.
I have few very small tables (a total of ~1000 rows) that I want to backup regularly into the same DB, to a single table. I know it sounds weird but hear me out.
Let's say that the tables I want to backup are named linux_commands, and windows_commands. These two tables have roughly: id (pkey), name, definition, config (jsonb), commands.
I want to back these up everyday into a table called commands_backup and I want this new table to have a date field, a field for windows_commands, and another one for linux_commands, so three columns in total. Each day, a script would run and write current date to date field, and then fetch whole linux_commands table and write it to related field in a single row, then do the same for windows_commands.
How would you setup something like this? Also, what is the best data type for storing whole data set in a single item?
In the target table, windows_commands and linux_commands should be type jsonb.
Then you can use:
INSERT INTO commands_backup VALUES (
current_date,
(SELECT jsonb_agg(to_jsonb(linux_commands)) FROM linux_commands),
(SELECT jsonb_agg(to_jsonb(windows_commands)) FROM windows_commands)
);
I would like to backfill a table to all dates in HDB. but the table has like 100 columns. What's the fastest way to backfill with the existing table?
I tried to get the schema from the current table and use the schema to backfill but doesn't work.
this is what I tried:
oldTable:0#newTable;
addtable[dbdir;`table;oldTable]
but this doesn't work. Any good way?
Does the table exist within the latest date partition of the HDB?
If so .Q.chk will add tables to partitions in which they are missing.
https://code.kx.com/q/ref/dotq/#qchk-fill-hdb
And with regards to addtable, what specific error are you getting when trying the above?
Redshift documentation identifies time-series tables as a best practice:
http://docs.aws.amazon.com/redshift/latest/dg/c_best-practices-time-series-tables.html
However, it doesn't address any of the following issues:
how many tables within a union-all view is reasonable - hundreds? (unanswered)
any method of writing to the union-all view and having redshift direct those inserts to the correct underlying tables? (Answer: no)
most effective method of loading underlying tables? Perhaps using firehose to insert into a staging table then periodically inserting those rows into appropriate table within union-all view? (unanswered)
any way to enable redshift to eliminate some underlying partitions (tables) when querying the union-all view if their date range is outside of a query's criteria? (Answer: No)
can redshift support dropping old tables, adding new tables and rebuilding union-all view within a transaction? (unanswered)
My situation:
100 million rows added daily, which will grow to 500 million in 3 years
12 month retention desired
Estimated 99% of all queries will hit the most recent 1-7 days
Data is written to existing table via kinesis firehose to s3 which then triggers a copy to redshift table.
My proposed solution:
Create a year of daily tables with a union all view, along with a dist_key of sensor_id (100,000+ uniq values) and a sort_key of (timestamp, sensor_id).
Have firehose load into staging table
Create separate process that once an hour queries staging table to discover dates of data within table, then performs an insert into 'appropriate table' select * from where timestamp = table's timestamp.
This hourly writer can probably wrap a table rename, multiple insert-selects, and table recreate in a transaction to be invisible to firehose.
Once a month drop old tables, create next month of tables, and rebuild view.
This union-all view maintenance can probably be wrapped in a transaction to avoid impacts to users.
Once a night run the vacuum analyzer.
EDITS: added notes identifying which issues have been answered, and added some detail to the proposed solution.
Your proposed process sounds quite good! While I can't answer all your questions, here is some information:
Any method of writing to the union-all view and having redshift direct those inserts to the correct underlying tables?
Views are read-only. It is not possible to write to a view, nor is it possible to insert data while expecting Redshift to send it to an appropriate table (eg a specific table for the given day).
Any way to enable redshift to eliminate some underlying partitions (tables) when querying the union-all view if their date range is outside of a query's criteria?
Redshift will not exclude specific tables from the query, but it will avoid reading particular disk blocks through the use of Zone Maps. Each block of data written to disk is associated with a specific table and column. The block has a Zone Map, which indicates the minimum and maximum values of that field stored within the block.
If a query includes a WHERE clause, Redshift can skip blocks that do not contain relevant data. This is particularly powerful when used on the SORTKEY column, since similar ranges of data are grouped together.
Given that you are using a date as the SORTKEY, Redshift will read very few disk blocks if the query includes a WHERE clause based on that column. This is very similar to the idea of skipping tables, but it actually skips reading disk blocks.
I am looking for a way to create a Redshift query that will retrieve data from a table that is generated daily. Tables in our cluster are of the form:
event_table_2016_06_14
event_table_2016_06_13
.. and so on.
I have tried writing a query that appends the current date to the table name, but this does not seem to work correctly (invalid operation):
SELECT * FROM concat('event_table_', to_char(getdate(),'YYYY_MM_DD'))
Any suggestions on how this can be performed are greatly appreciated!
I have tried writing a query that appends the current date to the
table name, but this does not seem to work correctly (invalid
operation):
Redshift does not support that. But you most likely won't need it.
Try the following (expanding on the answer from #ketan):
Create your main table with appropriate (for joins) DIST key, and COMPOUND or simple SORT KEY on timestamp column, and proper compression on columns.
Daily, create a temp table (use CREATE TABLE ... LIKE - this will preserve DIST/SORT keys), load it with daily data, VACUUM SORT.
Copy sorted temp table into main table using ALTER TABLE APPEND - this will copy the data sorted, and will reduce VACUUM on the main table. You may still need VACUUM SORT after that.
After that query your main table normally, probably giving it a range on timestamp. Redshift is optimised for these scenarios, and 99% of times you don't need to optimise table scans yourself - even on tables with billion of rows scans take milliseconds to few seconds. You may need to optimise elsewhere, but that's the second step.
To get insight in the performance of scans, use STL_QUERY system table to find your query ID, and then use STL_SCAN (or SVL_QUERY_SUMMARY) table to see how fast the scan was.
Your example is actually the main use case for ALTER TABLE APPEND.
I am assuming that you are creating a new table everyday.
What you can do is:
Create a view on top of event_table_* tables. Query your data using this view.
Whenever you create or drop a table, update the view.
If you want, you can avoid #2: Instead of creating a new table everyday, create empty tables for next 1-2 years. So, no need to update the view every day. However, do remember that there is an upper limit of 9,900 tables in Redshift.
Edit: If you always need to query today's table (instead of all tables, as I assumed originally), I don't think you can do that without updating your view.
However, you can modify your design to have just one table, with date as sort-key. So, whenever your table is queried with some date, all disk blocks that don't have that date will be skipped. That'll be as efficient as having time-series tables.