Object scriptability

1. A method comprising:

generating a graph-based ontological data structure including a plurality of nodes in a low-latency database analysis system, wherein nodes from the plurality of nodes represent respective analytical-objects in the low-latency database analysis system, wherein the nodes from the plurality of nodes are connected to other nodes from the plurality of nodes by edges, wherein an edge represents a relationship in the low-latency database analysis system, and wherein generating the graph-based ontological data structure includes:

receiving a high-level language script describing a first analytical-object in the low-latency database analysis system; and

in response to receiving the high-level language script:

generating a first node representing the first analytical-object; and

including the first node in the graph-based ontological data structure, wherein including the first node in the graph-based ontological data structure includes generating an edge in the graph-based ontological data structure indicating a relationship between the first node and a second node in the graph-based ontological data structure;

receiving data expressing a usage intent, the data expressing the usage intent including a request for data from the low-latency database analysis system;

obtaining results data from the low-latency database analysis system, wherein obtaining the results data from the low-latency database analysis system includes:

traversing the graph-based ontological data structure based on the data expressing the usage intent to identify the first node; and

in response to identifying the first node, obtaining the results data in accordance with the first analytical-object; and

outputting a response including the results data for presentation to a user.

2. The method of claim 1 , wherein the high-level language script is portable between different environments.

3. The method of claim 1 , wherein the high-level language script includes non-redundant terminology to describe the first analytical-object.

4. The method of claim 1 , wherein the high-level language script includes an alias, wherein the alias is a tuple representing parameters of the first analytical-object.

5. The method of claim 1 , wherein the high-level language script effectuates bulk changes across multiple analytical-objects.

6. The method of claim 1 , wherein the generating the graph-based ontological data structure further comprising:

obtaining object data for the first analytical-object from the graph-based ontological data structure;

generating the high-level language script describing the analytical-object based on the object data; and

outputting the high-level language script.

7. A low-latency database analysis system comprising:

a graph-based ontological data structure including a plurality of nodes, wherein nodes from the plurality of nodes represent respective analytical-objects in the low-latency database analysis system, wherein the nodes from the plurality of nodes are connected to to other nodes from the plurality of nodes by edges, wherein an edge represents a relationship in the low-latency database analysis system; and

a processor that executes instructions to:

in response to receiving a first high-level language script describing a first analytical-object for the low-latency database analysis system:

generate a first node representing the first analytical-object; and

include the first node in the graph-based ontological data structure, wherein including the first node in the graph-based ontological data structure includes generating an edge in the graph-based ontological data structure indicating a relationship between the first node and a second node in the graph-based ontological data structure;

in response to receiving first data expressing a usage intent, wherein the first data expressing the usage intent includes a request to output a second high-level language script representing a second analytical-object of the low-latency database analysis system:

obtain object data for the second analytical-object from the graph-based ontological data structure;

generate the second high-level language script describing the second analytical-object based on the object data; and

output the second high-level language script; and

in response to receiving second data expressing a usage intent, wherein the second data expressing the usage intent includes a request for data from the low-latency database analysis system:

obtain results data from the low-latency database analysis system, wherein to obtain the results data the processor executes the instructions to:

traverse the graph-based ontological data structure based on the second data expressing the usage intent to identify the first node; and

in response to identifying the first node, obtain the results data in accordance with the first analytical-object; and

output a response including the results data for presentation to a user.

8. The system of claim 7 , wherein:

the first high-level language script includes non-redundant terminology to describe the first analytical-object; and

the second high-level language script includes non-redundant terminology to describe the second analytical-object.

9. The system of claim 7 , wherein:

the first high-level language script includes a first alias, wherein the first alias is a tuple representing parameters of the first analytical-object; and

the second high-level language script includes a second alias, wherein the second alias is a tuple representing parameters of the second analytical-object.

10. The system of claim 7 , wherein the first high-level language script effectuates bulk changes across multiple analytical-objects.

11. A method comprising:

generating a graph-based ontological data structure including a plurality of nodes, wherein nodes from the plurality of nodes represent respective analytical-objects in the low-latency database analysis system, wherein the nodes from the plurality of nodes are connected to other nodes from the plurality of nodes by edges, wherein an edge represents a relationship in the low-latency database analysis system, and wherein generating the graph-based ontological data structure includes:

in response to receiving a first high-level language script describing a first analytical-object for the low-latency database analysis system:

generating a first node representing the first analytical-object; and

including the first node in the graph-based ontological data structure, wherein including the first node in the graph-based ontological data structure includes generating an edge in the graph-based ontological data structure indicating a relationship between the first node and a second node in the graph-based ontological data structure;

in response to receiving first data expressing a usage intent, wherein the first data expressing the usage intent includes a request to output a second high-level language script representing a second analytical-object of the low-latency database analysis system:

obtaining object data for the second analytical-object from the graph-based ontological data structure;

generating the second high-level language script describing the second analytical-object based on the object data; and

outputting the second high-level language script; and

in response to receiving second data expressing a usage intent, wherein the second data expressing the usage intent includes a request for data from the low-latency database analysis system:

obtaining results data from the low-latency database analysis system, wherein obtaining the results data from the low-latency database analysis system includes:

traversing the graph-based ontological data structure based on the second data expressing the usage intent to identify the first node; and

in response to identifying the first node, obtaining the results data in accordance with the first analytical-object; and

outputting a response including the results data for presentation to a user.

12. The method of claim 11 , wherein the second high-level language script has a defined format, is programmatically editable, and includes non-redundant terminology to describe the second analytical-object.

13. The method of claim 11 , wherein the second high-level language script includes an alias, wherein the alias is a tuple representing parameters of the second analytical-object.

14. The method of claim 11 , wherein obtaining the results data in accordance with the first analytical-object includes:

generating the results data by executing a data-query automatically generated in accordance with the first analytical-object.

15. The method of claim 14 , wherein obtaining the results data in accordance with the first analytical-object includes:

automatically generating data-query in accordance with the first analytical-object.

16. The method of claim 11 , wherein obtaining the results data in accordance with the first analytical-object includes:

obtaining one or more data values from a column of a table stored in a database accessed by the low-latency database analysis system, wherein the second node represents the column in the low-latency database analysis system.

17. The method of claim 1 , wherein obtaining the results data in accordance with the first analytical-object includes:

generating the results data by executing a data-query automatically generated in accordance with the first analytical-object.

18. The method of claim 17 , wherein obtaining the results data in accordance with the first analytical-object includes:

automatically generating data-query in accordance with the first analytical-object.

19. The method of claim 1 , wherein obtaining the results data in accordance with the first analytical-object includes:

obtaining one or more data values from a column of a table stored in a database accessed by the low-latency database analysis system, wherein the second node represents the column in the low-latency database analysis system.

20. The low-latency database analysis system of claim 7 , wherein to obtain the results data in accordance with the first analytical-object the processor executes the instructions to:

generate the results data by executing a data-query automatically generated in accordance with the first analytical-object.