.stream()
Stream records from your database to be consumed one at a time or in batches, without first having to buffer the entire result set in memory.
await Something.stream(criteria)
.eachRecord(async (record)=>{
});
Usage
| Argument | Type | Details | |
|---|---|---|---|
| 1 | criteria | The Waterline criteria to use for matching records in the database. |
Iteratee
Use one of the following:
.eachRecord(async (record)=>{ ... }).eachBatch(async (records)=>{ ... })
The custom function you provide to eachRecord() or eachBatch() will receive the following arguments:
| Argument | Type | Details | |
|---|---|---|---|
| 1 | record or records | The current record, or the current batch of records. A batch array will always contain at least one record, and it will never contain more records than the batch size (thirty by default). |
Errors
| Name | Type | When? |
|---|---|---|
| UsageError | Thrown if something invalid was passed in. | |
| AdapterError | Thrown if something went wrong in the database adapter. | |
| Error | Thrown if anything else unexpected happens. |
See Concepts > Models and ORM > Errors for examples of negotiating errors in Sails and Waterline.
When should I use this?
The .stream() method is almost exactly like .find(), except that it fetches records one batch at a time. Every time a batch of records is loaded, the iteratee function you provided is called one or more times. If you used .eachRecord(), your per-record function will be called once for each record in the batch. Otherwise, using .eachBatch(), your per-batch function will be called once with the entire batch.
This is useful for working with very large result sets, the kind that might overflow your server's available RAM if you tried to hold the entire set in memory at the same time. You can use Waterline's .stream() method to do the kinds of things you might already be familiar with from Mongo cursors: preparing reports, looping over and modifying database records in a shell script, moving large amounts of data from one place to another, performing complex transformations, or even orchestrating map/reduce jobs.
Examples
We explore four example situations below:
Basic usage
An action that iterates over users named Finn in the database, one at a time:
await User.stream({name:'Finn'})
.eachRecord(async (user)=>{
if (Math.random() > 0.5) {
throw new Error('Oops! This is a simulated error.');
}
sails.log(`Found a user ${user.id} named Finn.`);
});
Generating a dynamic sitemap
An action that responds with a dynamically generated sitemap:
// e.g. in an action that handles `GET /sitemap.xml`:
var sitemapXml = '<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">';
await BlogPost.stream()
.limit(50000)
.sort('title ASC')
.eachRecord((blogPost)=>{
sitemapXml += (
'<url>\n'+
' <loc>https://blog.example.com/' + _.escape(encodeURIComponent(blogPost.slug))+'</loc>\n'+
' <lastmod>'+_.escape(blogPost.updatedAt)+'</lastmod>\n'+
'<changefreq>monthly</changefreq>\n'+
'</url>'
);
});
sitemapXml += '</urlset>';
With .populate()
A snippet of a command-line script that searches for creepy comments from someone named "Bailey Bitterbumps" and reports them to the authorities:
// e.g. in a shell script
var numReported = 0;
await Comment.stream({ author: 'Bailey Bitterbumps' })
.limit(1000)
.skip(40)
.sort('title ASC')
.populate('attachedFiles', {
limit: 3,
sort: 'updatedAt'
})
.populate('fromBlogPost')
.eachRecord(async (comment)=>{
var isCreepyEnoughToWorryAbout = comment.rawMessage.match(/creepy/) && comment.attachedFiles.length > 1;
if (!isCreepyEnoughToWorryAbout) {
return;
}
await sails.helpers.sendTemplateEmail.with({
template: 'email-creepy-comment-notification',
templateData: {
url: `https://blog.example.com/${comment.fromBlogPost.slug}/comments/${comment.slug}.`
},
to: '[email protected]',
subject: 'Creepy comment alert'
});
numReported++;
});
sails.log(`Successfully reported ${numReported} creepy comments.`);
Batch-at-a-time
If we ran the code in the previous example, we'd be sending one email per creepy comment... which could be a lot, knowing Bailey Bitterbumps. Not only would this be slow, it could mean sending thousands of individual API requests to our transactional email provider, quickly overwhelming our API rate limit.
For this case, we could use .eachBatch() to grab the entire batch of records being fetched, rather than processing individual records one at a time, dramatically reducing the number of necessary API requests.
Configuring batch size
By default, .stream() uses a batch size of 30. That means it will load up to 30 records per batch; thus, if you are using .eachBatch(), your custom function will receive between 1 and 30 records each time it is called.
To increase or decrease the batch size, pass an additional argument to .eachBatch():
.eachBatch(100, async (records)=>{
console.log(`Got ${records.length} records.`);
})
Using
.eachBatch()in your code is not necessarily more or less efficient than using.eachRecord(). That's because, regardless which iterator you use, Waterline asks the database for more than one record at a time (30, by default). With.eachBatch(), you can easily configure this batch size using the extra argument described above. It's also possible to customize the batch size while using.eachRecord(for example, to avoid getting rate-limited by a 3rd party API you are using). Just use.meta(). For example,.meta({batchSize: 100}).
Notes
- This method can be used with
await, promise chaining, or traditional Node callbacks..stream()bails and throws an error immediately upon receiving the first error from any iteratee..stream()runs the provided iteratee function on each record or batch, one at a time, in series. Prior to Sails 1.1.0, the recommended usage of.stream()expected the iteratee to invoke a callback (next), which is provided as the second argument. This is no longer necessary as long as you do not actually include a second argument in the function signature.- Prior to Sails v1.0 / Waterline 0.13, this method had a lower-level interface, exposing a Readable "object stream". This was powerful, but tended to be error-prone. The new, adapter-agnostic
.stream()does not rely on emitters or any particular flavor of Node streams. (Need to get it working the old way? Don't worry, with a little code, you can still easily build a streams2/streams3-compatible Readable "object stream" using the new interface.)- Read more background about the impetus for creating
.stream()here, including additional examples, background information, and implementation details.
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