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We have some in memory tables to store aggregated data about transactions or even for indexing.
However, we need to be careful on the amount of data stored and think for an off-loading strategy.
There are two main areas which are more consuming the memory:
DB: with the indexing of the file position for each tx
Oracle: with the storage of all aggregated data
For the oracle, we might decide to keep only the 2-3 latest results of the polling and provide functions to read from the disk the previous data.
For the DB, we might have a strategy in terms of capacity of the ETS table. A max capacity of how many transactions should be hold can be implemented.
Moreover, the most frequent used transactions are the latest. So, we might have a strategy to hold only the last transaction of a chain in memory.
The text was updated successfully, but these errors were encountered:
We have some in memory tables to store aggregated data about transactions or even for indexing.
However, we need to be careful on the amount of data stored and think for an off-loading strategy.
There are two main areas which are more consuming the memory:
For the oracle, we might decide to keep only the 2-3 latest results of the polling and provide functions to read from the disk the previous data.
For the DB, we might have a strategy in terms of capacity of the ETS table. A max capacity of how many transactions should be hold can be implemented.
Moreover, the most frequent used transactions are the latest. So, we might have a strategy to hold only the last transaction of a chain in memory.
The text was updated successfully, but these errors were encountered: