TheNeuralBit commented on a change in pull request #10147:
URL: https://github.com/apache/arrow/pull/10147#discussion_r622292569
##########
File path: js/README.md
##########
@@ -147,37 +147,33 @@ for (let i = -1, n = column.length; ++i < n;) {
}
```
-### Usage with MapD Core
+### Usage with OmniSciDB
+
+OmniSciDB is an open source SQL-based, relational, columnar database engine
that leverages the full performance and parallelism of modern hardware (both
CPUs and GPUs) to enable querying of multi-billion row datasets in
milliseconds.
+
+OmniSciDB's [JavaScript
connector](https://www.npmjs.com/package/@mapd/connector) provides support for
receiving query results as an Arrow table:
```js
-import MapD from 'rxjs-mapd';
+import MapdCon from '@mapd/connector';
import { Table } from 'apache-arrow';
-const port = 9091;
-const host = `localhost`;
-const db = `mapd`;
-const user = `mapd`;
-const password = `HyperInteractive`;
-
-MapD.open(host, port)
- .connect(db, user, password)
- .flatMap((session) =>
- // queryDF returns Arrow buffers
- session.queryDF(`
+const session = new MapdCon()
+ .protocol("https")
+ .host("localhost")
+ .port("9091")
+ .dbName("omnisci")
+ .user("admin")
+ .password("HyperInteractive")
+ .connect()
+
+const table = session.queryDF(`
SELECT origin_city
- FROM flights
- WHERE dest_city ILIKE 'dallas'
- LIMIT 5`
- ).disconnect()
- )
- .map(([schema, records]) =>
- // Create Arrow Table from results
- Table.from([schema, records]))
- .map((table) =>
- // Stringify the table to CSV with row numbers
- table.toString({ index: true }))
- .subscribe((csvStr) =>
- console.log(csvStr));
+ FROM flights
+ WHERE dest_city ILIKE 'dallas'
+ LIMIT 5
+`)
+
+console.log(table.toString({ index: true }))
Review comment:
I think it's a little odd to have a usage example for another library
here. What do you think about just linking to the mapd/connector documentation
and explaining it returns arrow JS objects?
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