Hi, I am running Postgres 13 on CentOS 7, installed from the yum.postgresql.org <http://yum.postgresql.org/> repo.
I have 3 servers in different sites, with asynchronous replication managed by pgpool 4.0.11. Highest latency is 16ms RTT - but shouldn’t matter as it’s async - but my application is running many concurrent connections to keep throughput up when latency is high - as the queries are typically very fast. Servers have 32GB RAM and 4 cores. max_connections = 256 max_locks_per_transaction = 256 max_stack_depth = 2MB max_wal_senders = 5 max_wal_size = 1GB min_wal_size = 80MB shared_buffers = 10GB I am storing internet usage data and attributes from RADIUS messages. Our ingest does not come directly from the RADIUS server - instead it comes via rabbitmq which allows the database performance to drop without impacting our RADIUS services. I am making pretty heavy use of partitions to manage this. The data usage accounting is stored in a partition per hour, which runs through daily aggregation - aggregating usage older than 3 to usage per day, rather than usage per hour. At this point, we have a partition per day, rather than per hour. I am doing this, because we want hourly usage for 3 days, and RADIUS data comes in as a running total rather than a delta per some interval, and we want to have the deltas available. For each incoming record, we look at previous hours to find a previous total, and calculate a delta from that - this means we are regularly (hundred of times per second) looking at the last 2 hours. Partitioning per hour lets us manage that. I partition on an “interval_start” timestamp column. The aggregation process which runs each night at some time after midnight does the following, in a transaction: 1) Create a new “full day” partition for 3 days ago. 2) Look at hourly data older than 3 days (so, across 24 partitions) and calculate totals for that internet session on that day and insert in to the “full day” table. 3) Detach the hourly partitions 4) Attach the full day partition 5) Drop the hourly partitions This process takes around 12s to run - aggregating around 16.8M rows in to 700k rows. Each partition is around 70MB. Through this process, data continues to be ingested - but as the data being aggregated is 3+ days old, and the ingest process is only looking at current partitions (i.e. last couple hours), we don’t have any conflicts here. Additionally (and I think less importantly so skip this if it’s getting long winded) we store all RADIUS attributes from the RADIUS messages as jsonb, doing deduplication - most RADIUS messages (apart from the usage, session timers, etc.) are static, so we have a table of those “static” attributes in a jsonb column, and a table of events matching timestamps to sets of RADIUS messages. These tables also make use of partitioning - we have hot, cold, and frozen data so that only the hot and cold indexes are in memory most of the time - the “frozen” data is very large (100s of GB) so is only used for reporting. There are a couple of processes which run over this data periodically: 1) Move data from hot -> cold -> frozen as it ages 2) Aggregate “events” (i.e. RADIUS message timestamp to attributes) together so that longer term we don’t have a row per message, and rather only a row each time the attributes for a RADIUS session changes. This means there is always dead rows in this table, but they regularly get re-used. This appears to work very well, these processes run every 5 mins or so. The event aggregation process drops around 180k rows. The issue I am having, is that when the daily data usage aggregation runs, sometimes we have a big performance impact, with the following characteristics which happen *after* the aggregation job runs in it usual fast time of 12s or so: - The aggregation runs fast as per normal - Load on the server goes to 30-40 - recall we have quite high “max connections” to keep throughput high when the client is far (16ms) from the server - IOWait drops to ~0% (it’s usually at around 1-2%) but actual disk IO rates seem approx normal - User and System CPU increase to a total of 100% - ~86% and ~14% respectively - Processing time for RADIUS messages increases, and a big processing backlog builds - Swap is not used - it is enabled, but very low swap IO - Memory usage does not change If I stop the ingest process briefly, then start it up again, the problem goes away - the database server drops to 0% CPU, then after starting the ingest process the backlog clears very rapidly and performance is back to normal. This happens maybe once or twice a week - it’s not every day. It’s not on specific days each week. There is a vague correlation with other aggregation jobs (i.e. event aggregation mentioned above) running immediately after the daily data usage aggregation. Only one of these jobs runs at once - so if another scheduled job wants to run, it will run immediately after whatever is already running. I am wondering if there’s some sort of problem where we drop all these partitions, and postgres needs to do some work internally to free the space up or something, but has a hard time doing so with all the updates going on? I am not clear why this only happens some days - I am working on seeing if I can firm up (or rule out) the correlation with other aggregation jobs running immediately afterwards. Can anyone recommend some things to look at here? I’ve got quite a bit of metrics collected every minute - per-table io (i.e. hit/read), index sizes, table sizes, etc. - however everything there seems “normal” for the slow ingest rate when the issue occurs, so it’s hard to differentiate between cause and symptoms in those metrics. I have bumped up effective_cache_size from default of 4GB to 16GB since this last happened - but given IO doesn’t appear to be an issue, I don’t think this will have too much effect. -- Nathan Ward
