1 ACU in v1), capacity increments of as small as 0.5 ACU (vs. In addition to gaining access to v2 features, most Aurora Serverless customers can lower costs by moving to v2 due to a lower starting capacity of 0.5 ACU (vs. The prices below apply to both the MySQL- compatible and the PostgreSQL-compatible editions of Amazon Aurora, except where noted.Īurora Serverless v2 instantly scales databases to support hundreds of thousands of transactions per second and supports all features of Aurora, including Multi-AZ deployments, Read Replicas, Global Database, and RDS Proxy. Both scale automatically and you pay only for the storage and I/Os your Amazon Aurora database consumes.Īdditional charges apply for specific features including Global Database, Backtrack, and Snapshot Export, as well as for data transfer out of Aurora. You do not need to provision either storage or I/Os in advance. Alternatively, Aurora Serverless automatically starts up, shuts down, and scales capacity up or down based on your application's needs and you pay only for capacity consumed.Īurora storage is billed in per GB-month increments, while I/Os consumed are billed in per million request increments. I've got a snapshot of the database, so can provide more information if needed.Amazon Aurora is a modern relational database service that offers performance and high availability at scale, fully open-source MySQL- and PostgreSQL-compatible editions, and a range of developer tools for building serverless and machine learning-driven applications.įor provisioned Aurora, you can choose On-Demand Instances and pay for your database by the hour with no long-term commitments or upfront fees, or choose Reserved Instances for additional savings. I'm not sure if it's helpful but here's Transaction Logs Disk Usage from the period. Scheduled_events | 12.1 | 20 GB | scheduled_events_by_schedule_idx | 3.7 | 2701 MB Scheduled_events | 12.1 | 20 GB | scheduled_events_pkey | 0.5 | 0 Scheduled_events | 12.1 | 20 GB | scheduled_events_by_processed_effective_datetime_idx | 3.5 | 2502 MB Table_name | table_bloat | table_waste | index_name | index_bloat | index_waste select where processed = 'f' and effective_datetime = '' and effective_datetime 0 AND s2.schemaname = s.schemaname AND s2.tablename = s.tablename) AS nullhdr FROM pg_stats s, ( SELECT (SELECT current_setting(''block_size'')::numeric) AS bs, CASE WHEN substring(v,12,3) IN (''8.0'',''8.1'',''8.2'') THEN 27 ELSE 23 END AS hdr, CASE WHEN v ~ ''mingw32'' THEN 8 ELSE 4 END AS ma FROM (SELECT version() AS v) AS foo) AS constants GROUP BY 1,2,3,4,5) AS foo) AS rs JOIN pg_class cc ON cc.relname = rs.tablename JOIN pg_namespace nn ON cc.relnamespace = nn.oid AND nn.nspname = rs.schemaname AND nn.nspname ''information_schema'' LEFT JOIN pg_index i ON indrelid = cc.oid LEFT JOIN pg_class c2 ON c2.oid = i.indexrelid) AS sml ORDER BY CASE WHEN relpages :bloat.Two connections constantly (every 500ms) process the table: The table stores events (rows) to be processed in the future, when their effective_datetime is in the past. "scheduled_events_by_schedule_idx" btree (schedule_id) "scheduled_events_by_processed_effective_datetime_idx" btree (processed, effective_datetime) "scheduled_events_pkey" PRIMARY KEY, btree (event_id) Payload | jsonb | | not null | | extended | | Processed | boolean | | not null | false | plain | | Schedule_id | character varying(128) | | not null | | extended | |Įffective_datetime | timestamp without time zone | | not null | | plain | | Transaction_id | character varying(36) | | not null | | extended | |Ĭreated_at | timestamp without time zone | | not null | | plain | | Partition | integer | | not null | 0 | plain | | After the crash there were 11M rows.Ĭolumn | Type | Collation | Nullable | Default | Storage | Stats target | Description I couldn't execute selects in the table in a reasonably finite time(it took 45 minutes for "select count(*)" to finish). What I've found out is one of tables, where we put time-based entries (explained in a moment) grew to 72 GB. This problem follows me all the time and doesn't allow me to sleep. Please help me understand what has happened or point me to where I can learn about it to understand. There was no gigantic data load into the DB or something like that. Suddenly, during one day all the free storage was eaten by PostgreSQL (so it seems) and even after adding another 100GB of storage, the free space was still consumed. The DB had 100 GB storage of which around 30 GB was used.
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