gristlabs_grist-core/app/server/lib/GoogleExport.ts

82 lines
2.8 KiB
TypeScript
Raw Normal View History

import {drive} from '@googleapis/drive';
import {ActiveDoc} from 'app/server/lib/ActiveDoc';
import {RequestWithLogin} from 'app/server/lib/Authorizer';
(core) For exporting XLSX, do it memory-efficiently in a worker thread. Summary: - Excel exports were awfully memory-inefficient, causing occasional docWorker crashes. The fix is to use the "streaming writer" option of ExcelJS https://github.com/exceljs/exceljs#streaming-xlsx-writercontents. (Empirically on one example, max memory went down from 3G to 100M) - It's also CPU intensive and synchronous, and can block node for tens of seconds. The fix is to use a worker-thread. This diff uses "piscina" library for a pool of threads. - Additionally, adds ProcessMonitor that logs memory and cpu usage, particularly when those change significantly. - Also introduces request cancellation, so that a long download cancelled by the user will cancel the work being done in the worker thread. Test Plan: Updated previous export tests; memory and CPU performance tested manually by watching output of ProcessMonitor. Difference visible in these log excerpts: Before (total time to serve request 22 sec): ``` Telemetry processMonitor heapUsedMB=2187, heapTotalMB=2234, cpuAverage=1.13, intervalMs=17911 Telemetry processMonitor heapUsedMB=2188, heapTotalMB=2234, cpuAverage=0.66, intervalMs=5005 Telemetry processMonitor heapUsedMB=2188, heapTotalMB=2234, cpuAverage=0, intervalMs=5005 Telemetry processMonitor heapUsedMB=71, heapTotalMB=75, cpuAverage=0.13, intervalMs=5002 ``` After (total time to server request 18 sec): ``` Telemetry processMonitor heapUsedMB=109, heapTotalMB=144, cpuAverage=0.5, intervalMs=5001 Telemetry processMonitor heapUsedMB=109, heapTotalMB=144, cpuAverage=1.39, intervalMs=5002 Telemetry processMonitor heapUsedMB=94, heapTotalMB=131, cpuAverage=1.13, intervalMs=5000 Telemetry processMonitor heapUsedMB=94, heapTotalMB=131, cpuAverage=1.35, intervalMs=5001 ``` Note in "Before" that heapTotalMB goes up to 2GB in the first case, and "intervalMs" of 17 seconds indicates that node was unresponsive for that long. In the second case, heapTotalMB stays low, and the main thread remains responsive the whole time. Reviewers: jarek Reviewed By: jarek Differential Revision: https://phab.getgrist.com/D3906
2023-06-01 13:09:50 +00:00
import {streamXLSX} from 'app/server/lib/ExportXLSX';
import log from 'app/server/lib/log';
(core) support python3 in grist-core, and running engine via docker and/or gvisor Summary: * Moves essential plugins to grist-core, so that basic imports (e.g. csv) work. * Adds support for a `GRIST_SANDBOX_FLAVOR` flag that can systematically override how the data engine is run. - `GRIST_SANDBOX_FLAVOR=pynbox` is "classic" nacl-based sandbox. - `GRIST_SANDBOX_FLAVOR=docker` runs engines in individual docker containers. It requires an image specified in `sandbox/docker` (alternative images can be named with `GRIST_SANDBOX` flag - need to contain python and engine requirements). It is a simple reference implementation for sandboxing. - `GRIST_SANDBOX_FLAVOR=unsandboxed` runs whatever local version of python is specified by a `GRIST_SANDBOX` flag directly, with no sandboxing. Engine requirements must be installed, so an absolute path to a python executable in a virtualenv is easiest to manage. - `GRIST_SANDBOX_FLAVOR=gvisor` runs the data engine via gvisor's runsc. Experimental, with implementation not included in grist-core. Since gvisor runs on Linux only, this flavor supports wrapping the sandboxes in a single shared docker container. * Tweaks some recent express query parameter code to work in grist-core, which has a slightly different version of express (smoke test doesn't catch this since in Jenkins core is built within a workspace that has node_modules, and wires get crossed - in a dev environment the problem on master can be seen by doing `buildtools/build_core.sh /tmp/any_path_outside_grist`). The new sandbox options do not have tests yet, nor does this they change the behavior of grist servers today. They are there to clean up and consolidate a collection of patches I've been using that were getting cumbersome, and make it easier to run experiments. I haven't looked closely at imports beyond core. Test Plan: tested manually against regular grist and grist-core, including imports Reviewers: alexmojaki, dsagal Reviewed By: alexmojaki Differential Revision: https://phab.getgrist.com/D2942
2021-07-27 23:43:21 +00:00
import {optStringParam} from 'app/server/lib/requestUtils';
import {Request, Response} from 'express';
(core) For exporting XLSX, do it memory-efficiently in a worker thread. Summary: - Excel exports were awfully memory-inefficient, causing occasional docWorker crashes. The fix is to use the "streaming writer" option of ExcelJS https://github.com/exceljs/exceljs#streaming-xlsx-writercontents. (Empirically on one example, max memory went down from 3G to 100M) - It's also CPU intensive and synchronous, and can block node for tens of seconds. The fix is to use a worker-thread. This diff uses "piscina" library for a pool of threads. - Additionally, adds ProcessMonitor that logs memory and cpu usage, particularly when those change significantly. - Also introduces request cancellation, so that a long download cancelled by the user will cancel the work being done in the worker thread. Test Plan: Updated previous export tests; memory and CPU performance tested manually by watching output of ProcessMonitor. Difference visible in these log excerpts: Before (total time to serve request 22 sec): ``` Telemetry processMonitor heapUsedMB=2187, heapTotalMB=2234, cpuAverage=1.13, intervalMs=17911 Telemetry processMonitor heapUsedMB=2188, heapTotalMB=2234, cpuAverage=0.66, intervalMs=5005 Telemetry processMonitor heapUsedMB=2188, heapTotalMB=2234, cpuAverage=0, intervalMs=5005 Telemetry processMonitor heapUsedMB=71, heapTotalMB=75, cpuAverage=0.13, intervalMs=5002 ``` After (total time to server request 18 sec): ``` Telemetry processMonitor heapUsedMB=109, heapTotalMB=144, cpuAverage=0.5, intervalMs=5001 Telemetry processMonitor heapUsedMB=109, heapTotalMB=144, cpuAverage=1.39, intervalMs=5002 Telemetry processMonitor heapUsedMB=94, heapTotalMB=131, cpuAverage=1.13, intervalMs=5000 Telemetry processMonitor heapUsedMB=94, heapTotalMB=131, cpuAverage=1.35, intervalMs=5001 ``` Note in "Before" that heapTotalMB goes up to 2GB in the first case, and "intervalMs" of 17 seconds indicates that node was unresponsive for that long. In the second case, heapTotalMB stays low, and the main thread remains responsive the whole time. Reviewers: jarek Reviewed By: jarek Differential Revision: https://phab.getgrist.com/D3906
2023-06-01 13:09:50 +00:00
import {PassThrough, Stream} from 'stream';
/**
* Endpoint logic for sending grist document to Google Drive. Grist document is first exported as an
* excel file and then pushed to Google Drive as a Google Spreadsheet.
*/
export async function exportToDrive(
activeDoc: ActiveDoc,
req: Request,
res: Response
) {
// Token should come from auth middleware
(core) support python3 in grist-core, and running engine via docker and/or gvisor Summary: * Moves essential plugins to grist-core, so that basic imports (e.g. csv) work. * Adds support for a `GRIST_SANDBOX_FLAVOR` flag that can systematically override how the data engine is run. - `GRIST_SANDBOX_FLAVOR=pynbox` is "classic" nacl-based sandbox. - `GRIST_SANDBOX_FLAVOR=docker` runs engines in individual docker containers. It requires an image specified in `sandbox/docker` (alternative images can be named with `GRIST_SANDBOX` flag - need to contain python and engine requirements). It is a simple reference implementation for sandboxing. - `GRIST_SANDBOX_FLAVOR=unsandboxed` runs whatever local version of python is specified by a `GRIST_SANDBOX` flag directly, with no sandboxing. Engine requirements must be installed, so an absolute path to a python executable in a virtualenv is easiest to manage. - `GRIST_SANDBOX_FLAVOR=gvisor` runs the data engine via gvisor's runsc. Experimental, with implementation not included in grist-core. Since gvisor runs on Linux only, this flavor supports wrapping the sandboxes in a single shared docker container. * Tweaks some recent express query parameter code to work in grist-core, which has a slightly different version of express (smoke test doesn't catch this since in Jenkins core is built within a workspace that has node_modules, and wires get crossed - in a dev environment the problem on master can be seen by doing `buildtools/build_core.sh /tmp/any_path_outside_grist`). The new sandbox options do not have tests yet, nor does this they change the behavior of grist servers today. They are there to clean up and consolidate a collection of patches I've been using that were getting cumbersome, and make it easier to run experiments. I haven't looked closely at imports beyond core. Test Plan: tested manually against regular grist and grist-core, including imports Reviewers: alexmojaki, dsagal Reviewed By: alexmojaki Differential Revision: https://phab.getgrist.com/D2942
2021-07-27 23:43:21 +00:00
const access_token = optStringParam(req.query.access_token);
if (!access_token) {
throw new Error("No access token - Can't send file to Google Drive");
}
const mreq = req as RequestWithLogin;
const meta = {
docId: activeDoc.docName,
userId: mreq.userId,
altSessionId: mreq.altSessionId,
};
// Prepare file for exporting.
log.debug(`Export to drive - Preparing file for export`, meta);
(core) For exporting XLSX, do it memory-efficiently in a worker thread. Summary: - Excel exports were awfully memory-inefficient, causing occasional docWorker crashes. The fix is to use the "streaming writer" option of ExcelJS https://github.com/exceljs/exceljs#streaming-xlsx-writercontents. (Empirically on one example, max memory went down from 3G to 100M) - It's also CPU intensive and synchronous, and can block node for tens of seconds. The fix is to use a worker-thread. This diff uses "piscina" library for a pool of threads. - Additionally, adds ProcessMonitor that logs memory and cpu usage, particularly when those change significantly. - Also introduces request cancellation, so that a long download cancelled by the user will cancel the work being done in the worker thread. Test Plan: Updated previous export tests; memory and CPU performance tested manually by watching output of ProcessMonitor. Difference visible in these log excerpts: Before (total time to serve request 22 sec): ``` Telemetry processMonitor heapUsedMB=2187, heapTotalMB=2234, cpuAverage=1.13, intervalMs=17911 Telemetry processMonitor heapUsedMB=2188, heapTotalMB=2234, cpuAverage=0.66, intervalMs=5005 Telemetry processMonitor heapUsedMB=2188, heapTotalMB=2234, cpuAverage=0, intervalMs=5005 Telemetry processMonitor heapUsedMB=71, heapTotalMB=75, cpuAverage=0.13, intervalMs=5002 ``` After (total time to server request 18 sec): ``` Telemetry processMonitor heapUsedMB=109, heapTotalMB=144, cpuAverage=0.5, intervalMs=5001 Telemetry processMonitor heapUsedMB=109, heapTotalMB=144, cpuAverage=1.39, intervalMs=5002 Telemetry processMonitor heapUsedMB=94, heapTotalMB=131, cpuAverage=1.13, intervalMs=5000 Telemetry processMonitor heapUsedMB=94, heapTotalMB=131, cpuAverage=1.35, intervalMs=5001 ``` Note in "Before" that heapTotalMB goes up to 2GB in the first case, and "intervalMs" of 17 seconds indicates that node was unresponsive for that long. In the second case, heapTotalMB stays low, and the main thread remains responsive the whole time. Reviewers: jarek Reviewed By: jarek Differential Revision: https://phab.getgrist.com/D3906
2023-06-01 13:09:50 +00:00
const name = (optStringParam(req.query.title) || activeDoc.docName);
const stream = new PassThrough();
try {
// Send file to GDrive and get the url for a preview.
(core) For exporting XLSX, do it memory-efficiently in a worker thread. Summary: - Excel exports were awfully memory-inefficient, causing occasional docWorker crashes. The fix is to use the "streaming writer" option of ExcelJS https://github.com/exceljs/exceljs#streaming-xlsx-writercontents. (Empirically on one example, max memory went down from 3G to 100M) - It's also CPU intensive and synchronous, and can block node for tens of seconds. The fix is to use a worker-thread. This diff uses "piscina" library for a pool of threads. - Additionally, adds ProcessMonitor that logs memory and cpu usage, particularly when those change significantly. - Also introduces request cancellation, so that a long download cancelled by the user will cancel the work being done in the worker thread. Test Plan: Updated previous export tests; memory and CPU performance tested manually by watching output of ProcessMonitor. Difference visible in these log excerpts: Before (total time to serve request 22 sec): ``` Telemetry processMonitor heapUsedMB=2187, heapTotalMB=2234, cpuAverage=1.13, intervalMs=17911 Telemetry processMonitor heapUsedMB=2188, heapTotalMB=2234, cpuAverage=0.66, intervalMs=5005 Telemetry processMonitor heapUsedMB=2188, heapTotalMB=2234, cpuAverage=0, intervalMs=5005 Telemetry processMonitor heapUsedMB=71, heapTotalMB=75, cpuAverage=0.13, intervalMs=5002 ``` After (total time to server request 18 sec): ``` Telemetry processMonitor heapUsedMB=109, heapTotalMB=144, cpuAverage=0.5, intervalMs=5001 Telemetry processMonitor heapUsedMB=109, heapTotalMB=144, cpuAverage=1.39, intervalMs=5002 Telemetry processMonitor heapUsedMB=94, heapTotalMB=131, cpuAverage=1.13, intervalMs=5000 Telemetry processMonitor heapUsedMB=94, heapTotalMB=131, cpuAverage=1.35, intervalMs=5001 ``` Note in "Before" that heapTotalMB goes up to 2GB in the first case, and "intervalMs" of 17 seconds indicates that node was unresponsive for that long. In the second case, heapTotalMB stays low, and the main thread remains responsive the whole time. Reviewers: jarek Reviewed By: jarek Differential Revision: https://phab.getgrist.com/D3906
2023-06-01 13:09:50 +00:00
const [, url] = await Promise.all([
streamXLSX(activeDoc, req, stream, {tableId: ''}),
sendFileToDrive(name, stream, access_token),
]);
log.debug(`Export to drive - File exported, redirecting to Google Spreadsheet ${url}`, meta);
res.json({ url });
} catch (err) {
log.error("Export to drive - Error while sending file to GDrive", meta, err);
// Test if google returned a valid error message.
if (err.errors && err.errors.length) {
throw new Error(err.errors[0].message);
} else {
throw err;
}
}
}
// Creates spreadsheet file in a Google drive, by sending an excel and requesting for conversion.
(core) For exporting XLSX, do it memory-efficiently in a worker thread. Summary: - Excel exports were awfully memory-inefficient, causing occasional docWorker crashes. The fix is to use the "streaming writer" option of ExcelJS https://github.com/exceljs/exceljs#streaming-xlsx-writercontents. (Empirically on one example, max memory went down from 3G to 100M) - It's also CPU intensive and synchronous, and can block node for tens of seconds. The fix is to use a worker-thread. This diff uses "piscina" library for a pool of threads. - Additionally, adds ProcessMonitor that logs memory and cpu usage, particularly when those change significantly. - Also introduces request cancellation, so that a long download cancelled by the user will cancel the work being done in the worker thread. Test Plan: Updated previous export tests; memory and CPU performance tested manually by watching output of ProcessMonitor. Difference visible in these log excerpts: Before (total time to serve request 22 sec): ``` Telemetry processMonitor heapUsedMB=2187, heapTotalMB=2234, cpuAverage=1.13, intervalMs=17911 Telemetry processMonitor heapUsedMB=2188, heapTotalMB=2234, cpuAverage=0.66, intervalMs=5005 Telemetry processMonitor heapUsedMB=2188, heapTotalMB=2234, cpuAverage=0, intervalMs=5005 Telemetry processMonitor heapUsedMB=71, heapTotalMB=75, cpuAverage=0.13, intervalMs=5002 ``` After (total time to server request 18 sec): ``` Telemetry processMonitor heapUsedMB=109, heapTotalMB=144, cpuAverage=0.5, intervalMs=5001 Telemetry processMonitor heapUsedMB=109, heapTotalMB=144, cpuAverage=1.39, intervalMs=5002 Telemetry processMonitor heapUsedMB=94, heapTotalMB=131, cpuAverage=1.13, intervalMs=5000 Telemetry processMonitor heapUsedMB=94, heapTotalMB=131, cpuAverage=1.35, intervalMs=5001 ``` Note in "Before" that heapTotalMB goes up to 2GB in the first case, and "intervalMs" of 17 seconds indicates that node was unresponsive for that long. In the second case, heapTotalMB stays low, and the main thread remains responsive the whole time. Reviewers: jarek Reviewed By: jarek Differential Revision: https://phab.getgrist.com/D3906
2023-06-01 13:09:50 +00:00
async function sendFileToDrive(fileNameNoExt: string, stream: Stream, oauth_token: string): Promise<string> {
// Here we are asking google drive to convert excel file to a google spreadsheet
const requestBody = {
// name of the spreadsheet to create
name: fileNameNoExt,
// mime type of the google spreadsheet
mimeType: 'application/vnd.google-apps.spreadsheet'
};
// Define what gets send - excel file
const media = {
mimeType: 'application/vnd.ms-excel',
body: stream
};
const googleDrive = drive("v3");
const fileRes = await googleDrive.files.create({
requestBody, // what to do with file - convert to spreadsheet
oauth_token, // access token
media, // file
fields: "webViewLink" // return webViewLink after creating file
});
const url = fileRes.data.webViewLink;
if (!url) {
throw new Error("Google Api has not returned valid response");
}
return url;
}