System for managing vendor data
An electronic online system is configured to receive, at the electronic online system, an expression of a use case; determine, using a machine-learning technique with the expression of the use case as input, a data source and a time-to-live (TTL) value to satisfy the use case; and configure a data cache to store data received from the data source with the TTL value.
1. An electronic online system comprising:
a processor subsystem; and
a memory including instructions, which when executed by the processor subsystem, cause the processor subsystem to:
receive, at the electronic online system, an expression of a use case;
determine, using a machine-learning technique with the expression of the use case as input, a data source and a time-to-live (TTL) value to satisfy the use case;
configure a data cache to store data received from the data source with the TTL value;
receive, from an application, a read request for data in the data cache, the read request including the data source and a revised TTL value;
use the revised TTL value to train the machine-learning technique; and
configure the data cache to store data received from the data source with the revised TTL value.
2. The electronic system of claim 1 , wherein the expression of the use case is formed as a query.
3. The electronic system of claim 1 , wherein the expression of the use case is formed as a business objective.
4. The electronic system of claim 1 , wherein the expression of the use case is formed as a description of an output.
5. The electronic system of claim 1 , wherein the expression of the use case does not include the data source.
6. The electronic system of claim 1 , wherein the data source includes a database with a SQL database structure.
7. The electronic system of claim 1 , wherein the data source includes a database with a NoSQL database structure.
8. The electronic system of claim 1 , wherein the data cache includes an in-memory data structure store.
9. The electronic system of claim 8 , wherein the data in the data cache includes JavaScript Object Notation (JSON) documents.
10. The electronic system of claim 8 , wherein the data in the data cache includes JavaScript Object Notation (JSON) strings.
11. The electronic system of claim 1 , wherein the data cache includes a Redis data structure store.
12. The electronic system of claim 1 , wherein the machine-learning technique is trained to use a cost-benefit analysis to determine the TTL value for the use case.
13. The electronic system of claim 1 , wherein the memory includes instructions, which when executed by the processor subsystem, cause the processor subsystem to:
receive, from an application, a read request for data in the data cache;
determine that the data has expired based on a time-to-live (TTL) value corresponding to the data;
transmit a query to the application to determine whether to use the data even though the data has expired; and
conditionally refresh the data in the data cache based on a response to the query.
14. The electronic system of claim 1 , wherein the memory includes instructions, which when executed by the processor subsystem, cause the processor subsystem to:
receive, from an application, a read request for data in the data cache;
determine that the data has expired based on a time-to-live (TTL) value corresponding to the data;
refresh the data in the data cache; and
notify the application that the data in the data cache has been refreshed.
15. The electronic system of claim 1 , wherein the memory includes instructions, which when executed by the processor subsystem, cause the processor subsystem to:
receive, from an application, a read request for data in the data cache;
determine that the data has expired based on a time-to-live (TTL) value corresponding to the data;
refresh the data in the data cache;
determine a set of applications that use the data from the data cache; and
notify the set of applications that the data in the data cache has been refreshed.
16. A method performed on an electronic online system, the method comprising:
receiving, at the electronic online system, an expression of a use case;
determining, using a machine-learning technique with the expression of the use case as input, a data source and a time-to-live (TTL) value to satisfy the use case;
configuring a data cache to store data received from the data source with the TTL value;
receiving, from an application, a read request for data in the data cache, the read request including the data source and a revised TTL value;
using the revised TTL value to train the machine-learning technique; and
configuring the data cache to store data received from the data source with the revised TTL value.
17. The method of claim 16 , comprising:
receiving, from an application, a read request for data in the data cache;
determining that the data has expired based on a time-to-live (TTL) value corresponding to the data;
refreshing the data in the data cache;
determining a set of applications that use the data from the data cache; and
notifying the set of applications that the data in the data cache has been refreshed.
18. A non-transitory machine-readable medium comprising instructions, which when executed by a machine in an electronic online system, cause the machine to:
receive, at the electronic online system, an expression of a use case;
determine, using a machine-learning technique with the expression of the use case as input, a data source and a time-to-live (TTL) value to satisfy the use case;
configure a data cache to store data received from the data source with the TTL value;
receive, from an application, a read request for data in the data cache, the read request including the data source and a revised TTL value;
use the revised TTL value to train the machine-learning technique; and
configure the data cache to store data received from the data source with the revised TTL value.