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I am facing multiple performance issues with large datasets and high throughput. Basically it occurs because either the index is not present (default) and it takes too long to retrieve or because I define indexes (tried all kinds) and it takes too much time to recalculate.
I think AGE would leverage the combination with existing and optimized tables, such as using TimescaleDB hypertables as vertex.
I would like to understand what would stop AGE from following TimescaleDB approach, where you can do something like:
CREATETABLE "conditions"(
timeTIMESTAMP WITH TIME ZONENOT NULL,
device_id TEXT,
temperature NUMERIC,
humidity NUMERIC
);
CREATEINDEXON"conditions"(timeDESC);
CREATEINDEXON"conditions"(device_id, timeDESC);
-- 86400000000 is in usecs and is equal to 1 daySELECT create_hypertable('conditions', 'time', chunk_time_interval =>86400000000);
As you can see, the table creation process is detatched from the step to transform it to an hypertable. As long as you have a time column it is fine.
For AGE we could do the same, but instead of a time column we should require a agtype.
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I am facing multiple performance issues with large datasets and high throughput. Basically it occurs because either the index is not present (default) and it takes too long to retrieve or because I define indexes (tried all kinds) and it takes too much time to recalculate.
I think AGE would leverage the combination with existing and optimized tables, such as using TimescaleDB hypertables as vertex.
I would like to understand what would stop AGE from following TimescaleDB approach, where you can do something like:
As you can see, the table creation process is detatched from the step to transform it to an hypertable. As long as you have a time column it is fine.
For AGE we could do the same, but instead of a time column we should require a
agtype
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