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.
Related
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.
My use-case is that I need to copy a few columns from TABLE A to another TABLE B and also derive values of a few other columns of TABLE B by some calculation.
As per current estimation around 50,000 rows will be inserted on daily basis in TABLE A.
TABLE B should be updated with all data before End of day.
Hence, either I can use trigger which will be invoked on INSERT operation on TABLE A or schedule some Job at EOD which read all data in bulk from TABLE A , do some calculation and insert in TABLE B.
As I am new to trigger, I am not sure which option should i pick for this use-case. Any suggestion which would be a better approach ?
So far what I have read about triggers, they can slow down DBs performance if they are invoked frequently.
As around 50,000 insert operation will happen daily , so can I assume that 50,000 falls under heavy operations where triggers would not be beneficial ?
EDIT 1 : 50,000 insert operation will reach 100,000 insert operations daily
Postgres DB is used.
If you are doing bulk COPY into an unindexed table, adding a simple trigger will slow you down by a lot (like 5 fold). But if you are using single-row INSERTs or the table is indexed, the marginal slow down of adding a simple trigger will be pretty small.
50,000 inserts per day is not very many. You should have no trouble using a trigger on that, unless the trigger has to scan a large table on each call or something.
We have existing table of size more than 130 TB we have to delete records in DB2 . Using delete statement would will hang the system. So one way is we can partition the table month and year wise and then drop the partition one by one by using truncate or drop. Looking for a script which can create the partition and subsequently dropping.
You can't partition the data within an existing table. You would need to move the data to a new ranged partitioned table.
If using Db2 LUW, and depending on your specific requirments, consider using ADMIN_MOVE_TABLE to move your data to a new table while keeping your table "on-line"
ADMIN_MOVE_TABLE has the ability to add Range Partitioning and/or Multi-Dimentional Clustering on the new table during the move.
https://www.ibm.com/support/knowledgecenter/en/SSEPGG_11.5.0/com.ibm.db2.luw.sql.rtn.doc/doc/r0055069.html
Still, a 130 TB table is very large, and you would be well advised to be carful in planning and testing such a movement.
We have a table with nearly 2 billion events recorded. As per our data model, each event is uniquely identified with 4 columns combined primary key. Excluding the primary key, there are 5 B-tree indexes each on single different columns. So totally 6 B-tree indexes.
The events recorded span for years and now we need to remove the data older than 1 year.
We have a time column with long values recorded for each event. And we use the following query,
delete from events where ctid = any ( array (select ctid from events where time < 1517423400000 limit 10000) )
Does the indices gets updated?
During testing, it didn't.
After insertion,
total_table_size - 27893760
table_size - 7659520
index_size - 20209664
After deletion,
total_table_size - 20226048
table_size - 0
index_size - 20209664
Reindex can be done
Command: REINDEX
Description: rebuild indexes
Syntax:
REINDEX { INDEX | TABLE | DATABASE | SYSTEM } name [ FORCE ]
Considering #a_horse_with_no_name method is the good solution.
What we had:
Postgres version 9.4.
1 table with 2 billion rows with 21 columns (all bigint) and 5 columns combined primary key and 5 individual column indices with date spanning 2 years.
It looks similar to time-series data with a time column containing UNIX timestamp except that its analytics project, so time is not at an ordered increase. The table was insert and select only (most select queries use aggregate functions).
What we need: Our data span is 6 months and need to remove the old data.
What we did (with less knowledge on Postgres internals):
Delete rows at 10000 batch rate.
At inital, the delete was so fast taking ms, as the bloat increased each batch delete increased to nearly 10s. Then autovacuum got triggered and it ran for almost 3 months. The insert rate was high and each batch delete has increased the WAL size too. Poor stats in the table made the current queries so slow that they ran for minutes and hours.
So we decided to go for Partitioning. Using Table Inheritance in 9.4, we implemented it.
Note: Postgres has Declarative Partitioning from version 10, which handles most manual work needed in partitioning using Table Inheritance.
Please go through the official docs as they have clear explanation.
Simplified and how we implemented it:
Create parent table
Create child table inheriting it with check constraints. (We had monthly partitions and created using schedular)
Indexes are need to be created separately for each child table
To drop old data, just drop the table, so vacuum is not needed and will be instant.
Make sure to have the postgres property constraint_exclusion to partition.
VACUUM ANALYZE the old partition after started inserting in the new partition. (In our case, it helped the query planner to use Index-Only scan instead of Seq. scan)
Using Triggers as mentioned in the docs may make the inserts slower, so we deviated from it, as we partitioned based on time column, we calculated the table name at application level based on time value before every insert and it didn't affect the insert rate for us.
Also read other caveats mentioned there.
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.