Mongoose / MongoDB performance enhancements and tweaks (Part 3)

bush_doing_it_wrong_1

Holy crap. MongoDB native drivers are SO much faster than updates through mongoose’s ORM.  Initially, when I set out on this quest to enhance mongoDB performance with node.js, I thought that modifying my queries and limiting the number of results returned would be sufficient to scale. I was wrong (thanks Bush for the imagery). It turns out that the overhead that mongoose adds for wrapping mongoDB documents within mongoose’s ORM is tremendous. Well, I should say tremendous for our use case.

In my previous two posts of tweaking mongoDB / mongoose for performance enhancements (Part 1 and Part 2), I discussed optimization of queries or making simple writes instead of reads. These were worthwhile improvements and the speed difference eventually added up to significant chunks, but I had no idea moving to the native driver would give me these types of improvements (See below).

Example #1: ~400 streams, insertion times.
These numbers are from after I made the initial tweaks after Part 1. Unfortunately, I don’t really have that good of a printout from mongotop, but this kind of gives you an idea. Look at the write times for streams and packets, flowing at a rate of ~400 streams. This is for 400 sources of packets, which all gets written and persisted. Here you can see the write time to streams is @ 193ms / 400 streams or 48.25 ms / 100 streams. Likewise, packet writing is 7.25 ms / 100 streams. (You can mostly ignore read time, these are used for data aggregates and computing analytics). Compare these with the results below:

ns total read write
streams 193ms 0ms 193ms
packets 30ms 1ms 29ms
devices 9ms 9ms 0ms

Example 2: ~1000 streams, insertion times.
You can see here that write time has dropped significantly. Writes to the packets collection is hovering at around 1.7 ms / 1000 streams, and writes to the streams collection hovers at around 7.6 ms / 100 streams. Respectively, that’s a 425% and a 635% improvement in query write times to the packets collection and streams collection. And don’t forget, I had already begun the optimizations to mongoose. Even after the tweaks I made in Part 2, these numbers still represent a better than 100% improvement to query times. Huge, right?

ns total read write
packets 186ms 169ms 17ms
devices 161ms 159ms 2ms
streams 97ms 21ms 76ms

I knew using the mongoDB native drivers would be faster, but I hadn’t guessed that they would be this much faster.  

To make these changes, I updated mongoose to the latest version 3.8.14, which enables queries to be made using the native mongoDB driver released by 10gen (github here: https://github.com/mongodb/node-mongodb-native) via Model.Collection methods.  These in turn call methods defined in node_modules/mongodb/lib/mongodb/collection/core.js, which essentially just execute raw commands in mongo. Using these native commands, one can take advantage of things like bulk inserts.

I still like mongoose, because it helps instantiate the same object whenever you need to create and save something. If something isn’t defined in the mongoose.Schema, that object won’t get persisted to mongoDB either. Furthermore, it can still be tuned to be semi-quick, so it all depends on the use case. It just so happens that when you’re inserting raw json into mongoDB or don’t need the validation and other middleware that mongoose provides, you can use the mongoDB native drivers while still using mongoose for the good stuff. That’s cool.

Here’s what the new improvements look like:

    var Stream = mongoose.model('Stream');
    async.waterfall([
        //Native mongoDB update returns # docs updated, update by default updates 1 document:
        function query1 (callback) {
            Stream.collection.update(query1, query1set, {safe: true}, function(err, writeResult) {
                if (err) throw err;
                if (writeResult == 1) {
                    callback('Found and updated call @ Query1');
                } else {
                    callback(null);
                }
            });
        },
        function(callback) {
            Stream.collection.update(query2, query2set, {safe: true}, function(err, writeResult) {
                if (err) throw err;
                if (writeResult == 1) {
                    callback('Found and updated stream @ Query2');
                } else {
                    pushNewStream(packet, cb);
                    callback('No stream found.  Pushing new stream.');
                }
            });

        }
    ], function(err, results) {});

MongoDB performance enhancements and tweaks

MongoDB performance enhancements and tweaks

In my travails in building and my work on a real time analytics engine, I’ve formed some opinions on how well mongoDB is suited for scalability and how to tweak queries and my node.js code to extract some extra performance. Here are some of my findings, from several standpoints (mongoDB itself, optimizations to the mongoose driver for Node, and node.js itself).

Mongoose Driver
1. Query optimization

A. Instead of using Model.findOne or Model.find and iterating, try to use Model.find().limit() – I encountered a several factor speed up when doing this. This is talked about in several other places online.

B. If you have excess CPU, you can return a bigger chunk of documents and process them using your server instead and free up some cycles for MongoDB.

Improvement: Large (saw peaks of 1500ms for reads in one collection using mongotop. Afterwards, saw this drop to 200ms)

Example:

//Before:
Collection.findOne(query3, function(err, doc) {
  //Returns 1 mongoose document
});

//After
Collection.find(query3).limit(1).exec(function(err, docs) {
  //returns an array of mongoose documents            
});

See these links for some more information: Checking if a document exists – MongoDB slow findOne vs find

2. Use lean()
According to the docs, if you query a collection with lean(), plain javascript objects are returned and not mongoose.Document. I’ve found that in many instances, where I was just reading the data and presenting it to the user via REST or a visual interface, there was no need for the mongoose document because there was no manipulation after the read query.

Additionally, for relational data, if you have for instance a Schema that contains an array of refs (e.g. friends: [{ type: mongoose.ObjectId, ref: ‘User’}]), and you only need to return the first N number of friends to the user, you can use lean() to modify the returned javascript objects and then do population instead of populating the entire array of friends.

Improvement: Large (depending on how much data is returned)

Example:

//Before:
User.find(query, function(err, users) {
  //Users will be mongoose Documents. Hence you can't add fields outside the Schema (unless you have an { type: Any } object
  var options = {
     path: 'friends',
     model: 'User',
     select: 'first last'
  };
  Users.populate('friends', options, function(err, populated)) //will populate ALL friends in the array
});

//After
var query = new Query().lean();
User.find(query, function(err, users) {
  //Users will be javascript objects. Now you can go outside the schema and return data in line with what you need
  users.forEach(function(user) {
     user.friends = friends.splice(0, 10);  //take the first ten friends returned, or whatever
  });
  var options = {
     path: 'friends',
     model: 'User',
     select: 'first last'
  };
  Users.populate('friends', options, function(err, populated)) //now Model.populate populates a potentially much smaller array
});

Results (Example on my node.js server using mongoTop):
Load (ms)
Seconds No Lean() Lean()
5 561 524
10 371 303
15 310 295
20 573 563
25 292 291
30 302 291
35 544 520
40 316 307
45 289 286
50 537 503
Average 409.5 388.3
% improvement 0.051770452 = 5.177%

3. Keep mongoDB “warm”.
MongoDB implements pretty good caching. This can be evidenced by running a query several times in quick succession. When this occurs, my experience has been that the query time decreases (sometimes dramatically so). For instance, a query can go from 50ms to 10ms after running twice. We have one collection that is constantly queried – about 500 times per second for reads and also 500 times per second for writes. Keeping this collection “warm”, i.e. running the query that will be called at some point in the future, can help keep the call responsive when Mongo starts to slow down.

Improvement: Untested
Example:

function keepwarm() {
   setTimeout(function() {
      User.find(query);
      keepwarm();
   }, 500);
}

Mongo Native
1. Compound indexing
For heavy duty queries that run often, I decided to create compound indices using all the parameters that comprised the query. Even though intuitively, it didn’t jump out to me that indexing by timestamp for instance would make a difference, it does. According to the mongoDB documentation, if your query sorts based on timestamp (which ours did), indexing by timestamp can actually help.

Improvement: Large (depending on how large in documents your collection is and how efficiently mongoDB can make use of your indices)
Example:

//in mongo shell
db.collection.ensureIndex({'timestamp': 1, 'user': 1});

//in mongoose schema definition
Model.index({'timestamp': 1, 'user': 1});

Alternative? Aggregating documents into larger documents, such as time slices. Intuitively, that would mean that queries don’t have to traverse as large an index to reach the targeted documents. You may ask what the difference is between creating a compound index versus breaking the document down into aggregates like a day’s or hours slice. Here’s a few possibilities:

  1. A. MongoDB tries to match up queries with indices or compound indices, but there’s no guarantee that this match will occur. Supposedly, the algorithm used to determine which index to use is pretty good, but I question how good it is if for instance, the query you are using includes an additional parameter to search for. If MongoDB doesn’t see all parameters in the index, will it still know to use a compound index or a combination of compound indices?
  2. B. Using aggregates could actually be slower if it requires traversal of the document for the relevant flight data (which might not afford fast reads).
  3. C. If writes are very heavy for the aggregate (e.g. you use an aggregate document that is too large in scope), the constant reading and writing of the document may cause delays via mongoDB’s need to lock the collection/document.
  4. D. Aggregates could make indexing more difficult
  5. E. Aggregates could make aggregation/mapreduce more difficult because your document no longer represents a single instance of an “event” (or is not granular enough)

2. Use Mongotop to determine where your bottlenecks are.
Mongotop shows each collection in your database and the amount of time spent querying reads and writes. By default it updates every second. Bad things happen when the total query time jumps over a second. For instance, in Node, that means that the event queue will begin to block up because mongo is taking too long

Example:

 
//example output
                            ns       total        read       write		2014-07-31T17:02:06
              mean-dev.packets       282ms       282ms         0ms
             mean-dev.sessions         0ms         0ms         0ms
               mean-dev.series         0ms         0ms         0ms
              mean-dev.reduces         0ms         0ms         0ms
             mean-dev.projects         0ms         0ms         0ms

3. Use explain()… sparingly
I’ve found that explain is useful initially, because it will show you the number of scanned documents to reach the result of the query. However, when trying to optimize queries further, I found that it was not that useful. If I’ve already created my compound indices and MongoDB is using them, how can I extract further performance using explain() when explain() may already show a 0 – 1ms duration?

Example:

//in mongo shell
db.collection.find({
        $and: [{
            'from.ID': 956481854
        }, {
            'to.ID': 1038472857
        }, {
            'metadata.searchable': false
        }, {
            'to.IP_ADDRESS': '127.0.0.1'
        }, {
            'from.timestamp': {
                $lt: new Date(ISODate().getTime() - 1000 * 60 * 18)
            }
        }]
    }).explain()

4. For fast inserts for a collection of limited size, consider using a capped collection.

A capped collection in mongoDB is essentially a queue-like data structure that enforces first-in first-out. According to the mongoDB docs, capped collections maintain insertion order, so they’re perfect for time series. You just have to specify what the max size of the collection should be in bytes. I used an average based on: db.collection.stats(), where I found that each record was about 450 bytes in size.

To enforce this, you can run this in the mongoDB shell:

db.runCommand({"convertToCapped": "mycoll", size: 100000}); //size in bytes

See mongoDB docs here:

Node.js
1. Implement pacing for large updates.
I’ve found that in situations where there is a periodic update on a large subset of a collection while many updates are going on, the large update could cause the event queue in Node to backup as mongoDB tried to keep up. By throttling the number of updates that can go on based on total update time, I could adjust based on the load on the server currently. The philosophy is if node/mongoDB have extra cycles, we can dial up the pace of backfilling/updates a bit, whereas when node/mongoDB is overloaded, we can backoff.

Example:


//Runs periodically
    _aggregator.updateStatistics(undefined, updateStatisticsPace, function(result) {
          console.log('[AGGREGATOR] updateStatistics() complete.  Result: [Num Updated: %d, Duration: %d, Average (ms) per update: %d]', result.updated, result.duration, result.average);
          if (result.average < 5) {  //<5 ms, speed up by 10%
            updateStatisticsPace = Math.min(MAX_PACE, Math.floor(updateStatisticsPace * 1.1));    //MAX_PACE = all records updated
          } else if (result.average >= 5 && result.average < 10) { //5 < ms < 10, maintain pace
            updateStatisticsPace = Math.min(MAX_PACE, updateStatisticsPace);
          } else {  //>= 10ms, slow down by 2/3, to a min of 10
            updateStatisticsPace = Math.min(MAX_PACE, Math.max(updateStatisticsPace_min, Math.floor(updateStatisticsPace * .66)));
          }

          if (MAX_PACE === updateStatisticsPace) { console.log('[Aggregator] updateStatistics() - Max pace reached: ' + _count); }
          console.log('[AGGREGATOR] updateStatistics() Setting new pace: %d', updateStatisticsPace);
          callback(null, result)
    });