![]() ![]() For more information, see the DISTSTYLE parameter later in this topic. You can use the DISTKEY keyword after a column name or as part of the table definition by using the DISTKEY ( column_name) syntax. Only one column in a table can be the distribution key. Keyword that specifies that the column is the distribution key for the table. The distribution style that you select for tables affects the overall performance of your database. Amazon Redshift distributes the rows of a table to the compute nodes according to the distribution style specified for the table. Keyword that defines the data distribution style for the whole table. Use distsytle or distkey with sortkey – create table demystified Redshift Distribution Key – Choosing Best Distribution Style Redshit create table with emphasis on performance Insert into table from S3 COPY test_ers FROM 's3://ariel-s3-buket/tickitdb/allusers_pipe.txt' iam_role 'arn:aws:iam::527228915290:role/RedshiftAccessS3' delimiter '|' region 'us-east-1' Ĭasting select cast(pricepaid as integer) You need to 1) remove unneeded data, 2) move some data to S3 and use Spectrum, or 3) add a node you your cluster.Get list of databases select oid as database_id,Ĭonnect to db SELECT * FROM PG_TABLE_DEF WHERE schemaname ='public'Ĭheat sheet for basic SQL operations on Redshift.Ĭreate table create table test_ers(ĬTAS create table event_backup as select * from event ĬTAS with distkey and sory key create table myTable2 If you need more storage w/o more compute you can look at the storage optimized nodes but since you are at the very smallest end of Redshift these likely aren't a win for you. Migrating some data to S3 and using Spectrum to access could be an option. This means reducing a great deal of data on your disks or increasing your node count (and cost). #REDSHIFT VACUUM FULL#(I expect you realize this and this is just a start.) You should be operating below 70% disk full in most cases - this varies by workload and table sizes but is a good general rule. UNSOLICITED ADVICE: You are on the right track by freeing up disk space but you need to take more action than reducing this one table. But based on the description these don't seem likely. The table could live in S3 and be access through spectrum. There could be some some other misunderstanding happening. This is unlikely to be what is happening if you are seeing EXACTLY the same number of blocks but if you are just seeing changes in blocks that are less than you expect this could be in play. The table size will move in very large chunks and when removing rows the size may not change except in large chunks. Then it takes no additional disk space to add rows until these blocks start to fill up. This cluster has 4 slices and if this table is 1,500 columns wide (yes, that is silly wide) the table will take up 6,000 blocks to just store the first 4 rows. This matters because of how Redshift stores data - each block is 1MB in size and each column needs a block for its data. It is also possible that the table in question is very wide (many columns). This will cause the vacuum to run on the full set of rows. This will have a higher likelihood of success in a disk full situation.Īnother possibility is that the delete of rows didn't complete correctly or wasn't committed before the vacuum was run. In this case you can run a delete-only vacuum that will not attempt to sort the table, just reclaim disk space. So could it be that the vacuum you tried is not completing? You see vacuum need temp space to sort the table data and if you have a cluster that has gotten too full then the vacuum could fail. You mention that you are getting disk full messages even when vacuuming. Do I have this correct?įirst possibility is that the vacuum did not complete correctly. Once complete you didn't see any change to the cluster space nor the size of the table, not one block difference in table size. ![]() To address the disk space concern you deleted 1/2 of the rows in the table in question and then vacuumed the table. You have a table that is 10515 blocks in size. This is too full and needs to lowered to be lowered to provide temp space for query execution. You have a 2 node dc2.large cluster and it is 92% disk full. There are a few possibilities here but first let me check the facts. ![]()
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