createQueryStream loads a big set of data into memory before streaming

The bigQuery.createQueryStream seems to load an entire set of data into memory before the stream starts actually piping data into the next streams.

Environment details

• OS: MacOS 12.1
• Node.js version: 14.18.1
• npm version: 6.14.15
• @google-cloud/bigquery version: 5.10.0

Steps to reproduce

Using this test script I can see over 300mb of data is loaded into memory before the stream starts piping to the next streams. And I am only selecting one column, so this is a lot of records in that case.

If I log each entry in the transform stream it also seems to come into batches. It pauses for a while and suddenly starts piping again. This makes me think internally a whole page is loaded into memory and then piped to the readable stream, but this might not be the issue.

const stream = bigQuery
  .dataset("dataset")
  .createQueryStream("SELECT email FROM table");

let checked = false;
const tr = new Transform({
  objectMode: true,
  transform: (chunk, enc, next) => {
    if (!checked) {
      console.dir("START PIPING");
      console.dir(process.memoryUsage());
      console.dir(
        "DIFFERENCE = " +
          (process.memoryUsage().heapUsed - heapUsed) / (1024 * 1024) +
          " MB"
      );
      checked = true;
    }
    next(null, JSON.stringify(chunk) + "\n");
  },
});

const write = fs.createWriteStream("/dev/null");

console.dir("BEFORE");
console.dir(process.memoryUsage());
const { heapUsed } = process.memoryUsage();

tr.pipe(write);
stream.pipe(tr);

[rohit-gohri]

Is there a way to limit the size of this chunk of data? Because it is causing an OOM exception for us on some tables. Can we use maxResults for that or will it limit the total results?

[loferris]

The actual createQueryStream method doesn’t have this parameter available, as it’s packaging both query creation and use of the streamify API. One option would be to end the stream after a certain amount of data has been emitted and make separate calls of that method until all data has been read. (See an example). For further flexibility, you can also write the logic by hand using various query and results methods with streaming. Feel free to open up a follow up issue if a sample seems helpful! As to incorporating more configurability into the library itself for creating streamified queries, it’ll depend on requests!