If I have large amounts of data in a table defined like
CREATE TABLE sensor_values ( ts TIMESTAMPTZ(35, 6) NOT NULL,
value FLOAT8(17, 17) DEFAULT 'NaN' :: REAL NOT NULL,
sensor_id INT4(10) NOT NULL, );
Data comes in every minute for thousands of points. Quite often though I need to extract and work with daily values over years (On a web frontend). To aid this I would like a sensor_values_days table that only has the daily sums for each point and then I can use this for faster queries over longer timespans.
I don't want a trigger for every write to the db as I am afraid that would slow down the already bottle neck of writes to the db.
Is there a way to trigger only after so many rows have been inserted ?
Or perhaps an index and maintains a index of a sum of entries over days ? I don't think that is possible.
What would be the best way to do this. It would not have to be very up to date. Losing the last few hours or a day would not be an issue.
Thanks
What would be the best way to do this.
Install clickhouse and use AggregatingMergeTree table type.
With postgres:
Create per-period aggregate table. You can have several with different granularity, like hours, days, and months.
Have a cron or scheduled task run at the end of each period plus a few minutes. First, select the latest timestamp in the per-period table, so you know at which period to start. Then, aggregate all rows in the main table for periods that came after the last available one. This process will also work if the per-period table is empty, or if it missed the last update then it will catch up.
In order to do only inserts and no updates, you have to run it at the end of each period, to make sure it got all the data. You can also store the first and last timestamp of the rows that were aggregated, so later if you check the table you see it did use all the data from the period.
After aggregation, the "hour" table should be 60x smaller than the "minute" table, that should help!
Then, repeat the same process for the "day" and "month" table.
If you want up-to-date stats, you can UNION ALL the results of the "per day" table (for example) to the results of the live table, but only pull the current day out of the live table, since all the previous days's worth of data have been summarized into the "per day" table. Hopefully, the current day's data will be cached in RAM.
It would not have to be very up to date. Losing the last few hours or a day would not be an issue.
Also if you want to partition your huge table, make sure you do it before its size becomes unmanageable...
Materialized Views and a Cron every 5 minutes can help you:
https://wiki.postgresql.org/wiki/Incremental_View_Maintenance
In PG14, we will have INCREMENTAL MATERIALIZED VIEW, but for the moment is in devel.
Related
I have a table in Postgres database which has a lot of records (30,00,000+).
I want to delete all records which are older than an year but I see that there is no timestamp or date column in this table.
How can I delete the old records in this case? (first I want to get the count of records which are older than 1 year)
Also, will deleting huge number of records in a single SQL query cause performance issues while the deletion is in progress?
As #laurenze_albe has said, there is no way to get the age of a row of data. Probably, you'll end up deleting based on some kind of id field. If you have the time, you could monitor how many records are added during a week, and then make a guess for the number of records in a year.
If you get max(id) from today and then max(id) a week from now, you could subtract the difference and multiply by 52.
A row in a PostgreSQL table has no age unless you explicitly store it with the data, so there is no way to do that. You have to use a condition based on the data.
Deleting many data can take a long time, even if all foreign keys are indexed. The king's way to speedy mass deletions is table partitioning.
I'm storing a number of rows in a hypertable. The table size is growing quite large now even in its current test configuration.
I'd like to reduce the frequency of data from say once every 5 seconds to say once every 60 seconds for data older than a week by deleting a number of these older records.
Can anyone recommend an approach for doing so, or perhaps a better approach that better fits with timescaledb design?
So one of the next releases will have a bit in feature around data retention policies around continuous aggregations, so that you can define a continuous aggregation policy that rolls up secondly data into minutely data, then drop the secondly data that's older than some time period.
(That capability doesn't exist today with continuous aggs, but will very shortly. Right now the best approach is either to have some cron job that deletes old data, or one that copies from one table to a second while aggregating, then calling drop_chunks on the first table.)
Ok, I've read 2 minutes of timescaledb documentation, so I'm an expert, right. Here's what I propose:
You already have a table (I'll call it the business table) and a hypertable with raw 5-second data in it
Create a second hypertable with the same columns as the first hypertable
Insert into the 2nd hypertable using a 60-second windowing function and average, minimum, or maximum values for your readings data (you have to decide on which aggregation function is meaningful for your case.) This insert SQL looks something like:
INSERT into minute_table (timestamp, my_reading)
(SELECT time_bucket('60 seconds', time) as the_minute, avg(my_raw_reading)
FROM five_second_table
WHERE time < (now() - interval '1week')
GROUP BY the_minute
);
Next, delete from the 5-second hypertable where the timestamp in there is within any range of times in the 60-second hypertable.
Finally, schedule something like this to run every week.
Sorry I'm not fluent in all the timescaledb functions but this should get you started on the 'heavy lift' of manually aggregating up from 5-second to 60-second samples.
Take a look on Data Retention
For example:
SELECT drop_chunks(interval '24 hours', 'conditions');
This will drop all chunks from the hypertable 'conditions' that only include data older than this duration, and will not delete any individual rows of data in chunks.
Our web based app with 100,000 concurrent users has a use case where we auto-save the user's activity every 5 seconds. Consider a table like this:
create table essays
(
id uuid not null constraint essays_pkey primary key,
userId text not null,
essayparts jsonb default '{ }' :: jsonb,
create_date timestamp with time zone default now() not null,
modify_date timestamp with time zone default now() not null
);
create index essays_create_idx on essays ("create_date");
create index essays_modify_idx on essays ("modify_date");
This works well for us as all the stuff related to a user's essay such as title, brief byline. requestor, full essay body, etc. are all stored in the essayparts column as a JSON. For auto-saving the essay, we don't insert new rows all the time though. We update each ID (each essay) with all its components.
So there are plenty of updates per essay, as this is a time consuming and thoughtful activity. Given the auto save every 5 seconds, if a user was to be writing for half an hour, we'd have updated her essay around 360 times.
This would be fine with the "HOT" (heap only tuples) functionality of PostgreSQL. We're using v10 so we are fine. However, the challenge is that we also update the modify_date column every time the essay is saved and this has an index too. Which means by the principle of HOT this is not benefiting from the HOT update and a lot of fragmentation occurs.
I suppose in the web or mobile world this is not an unusual pattern. Many services seem to auto-save content. Are they insert only? If so, if the user logs out and comes back in, how do they show the records, by looking at the max(modify_date)? Or is there any other mechanism to leverage HOT updates while also updating an indexed column in the table?
Appreciate any pointers, thank you!
Performing an update every 5 second with 100000 concurrent users will produce 20000 updates per second. This is quite challenging as such, and you would need a good system to pull it off, but autovacuum will never be able to keep up if those updates are not HOT.
You have several options:
Choose a relational database management system other than PostgreSQL that updates rows in place.
Do not index modify_date and hope that HOT will do the trick.
Perform these updates way less often than once every 5 seconds (who needs auto-save every 5 seconds anyway?).
Auto-save the data somewhere else than in the database.
So I know that TTL is not available for counters because of design reasons and I've read https://issues.apache.org/jira/browse/CASSANDRA-2103 as well as some other SO questions regarding this but there seems to be no clear answer(unless I am missing something which is entirely plausible):
How do we elegantly handle the expiration of counters in Cassandra?
Example use case: page views on a specific day.
For this we might have a table such as
CREATE TABLE pageviews (page varchar, date varchar, views counter, PRIMARY KEY(page, date));
One year from now the information of how many views we had on one specific day is not very relevant (instead we might have aggregated it into a view/month table or similar) and we don't want unnecessary data hanging around in our db for no reason. Normally we would put a TTL on this and let Cassandra handle it for us - elegant! But since we aren't allowed to use TTL for counter tables this is not an option..
You also cant just run delete from pageviews where date > 'xxxx' since both key must be defined in the where clause.
You would first need to query all the page first then issue individual deletes, which is not scalable.
Is there any proper way of achieving this ?
Its significantly slower, but thats kinda the price if you dont want to manage the expiration yourself - you can use LWTs and actually insert TTL'd columns instead of updating a counter. ie:
CREATE TABLE pageviews (
page varchar,
date timestamp,
views int,
PRIMARY KEY(page, date))
WITH compaction = {'class': 'LeveledCompactionStrategy'};
To update a page view:
UPDATE pageviews USING TTL 604800
SET views = *12*
WHERE page = '/home' AND date = YYYY-MM-DD
IF views = *11*
if it fails, reread and try again. This can be very slow if high contention, but in that case you can do some batching per app, say only flush updates every 10 seconds or something and increment by more than 1 at a time
To see total in range of dates:
SELECT sum(views) FROM pageviews WHERE page='/home' and date >= '2017-01-01 00:00:00+0200' AND date <= '2017-01-13 23:59:00+0200'
Fastest approach would be to use counters and just have a job during a less busy time that deletes things older than X days.
Another idea if you are Ok with some % error, you can use a single counter per page and use forward decay to "expire" (make insignificant) old view increments, will still need a job to adjust landmark periodically though. This will not be as useful for looking at ranges though and will only give you an estimate of "total so far".
If you don't need date range queries, you can use a partition key of page % X, date and a clustering key of page.
Then for each date you wish to discard, you can delete partitions 0 through X - 1 with X delete statements.
I am trying to creating a way to convert bulk date queries into incremental query. For example, if a query has where condition specified as
WHERE date > now()::date - interval '365 days' and date < now()::date
this will fetch a years data if executed today. Now if the same query is executed tomorrow, 365 days data will again be fetched. However, I already have last 364 days data from previous run. I just want a single day's data to be fetched and a single day's data to be deleted from the system, so that I end up with 365 days data with better performance. This data is to be stored in a separate temp table.
To achieve this, I create an incremental query, which will be executed in next run. However, deleting the single date data is proving tricky when that "date" column does not feature in the SELECT clause but feature in the WHERE condition as the temp table schema will not have the "date" column.
So I thought of executing the bulk query in chunks and assign an ID to that chunk. This way, I can delete a chunk and add a chunk and other data remains unaffected.
Is there a way to achieve the same in postgres or greenplum? Like some inbuilt functionality. I went through the whole documentation but could not find any.
Also, if not, is there any better solution to this problem.
I think this is best handled with something like an aggregates table (I assume the issue is you have heavy aggregates to handle over a lot of data). This doesn't necessarily cause normalization problems (and data warehouses often denormalize anyway). In this regard the aggregates you need can be stored per day so you are able to cut down to one record per day of the closed data, plus non-closed data. Keeping the aggregates to data which cannot change is what is required to avoid the normal insert/update anomilies that normalization prevents.