Example

Simpler, Cheaper, Faster: Playtomic's Move from .NET to Node and Heroku

High Scalability

15 Oct 2012 — 11 min read

This is a guest post by Ben Lowry, CEO of Playtomic. Playtomic is a game analytics service implemented in about 8000 mobile, web and downloadable games played by approximately 20 million people daily.

Here's a good summary quote by Ben Lowry on Hacker News:

Just over 20,000,000 people hit my API yesterday 700,749,252 times, playing the ~8,000 games my analytics platform is integrated in for a bit under 600 years in total play time. That's just yesterday. There are lots of different bottlenecks waiting for people operating at scale. Heroku and NodeJS, for my use case, eventually alleviated a whole bunch of them very cheaply.

Playtomic began with an almost exclusively Microsoft.NET and Windows architecture which held up for 3 years before being replaced with a complete rewrite using NodeJS. During its lifetime the entire platform grew from shared space on a single server to a full dedicated, then spread to second dedicated, then the API server was offloaded to a VPS provider and 4 – 6 fairly large VPSs. Eventually the API server settled on 8 dedicated servers at Hivelocity, each a quad core with hyperthreading + 8gb of ram + dual 500gb disks running 3 or 4 instances of the API stack.

These servers routinely serviced 30,000 to 60,000 concurrent game players and received up to 1500 requests per second, with load balancing done via DNS round robin.

In July the entire fleet of servers was replaced with a NodeJS rewrite hosted at Heroku for a significant saving.

Scaling Playtomic with NodeJS

There were two parts to the migration:

Dedicated to PaaS: Advantages include price, convenience, leveraging their load balancing and reducing overall complexity. Disadvantages include no New Relic for NodeJS, very inelegant crashes, and a generally immature platform.
.NET to NodeJS: Switching architecture from ASP.NET / C# with local MongoDB instances and a service preprocessing event data locally and sending it to centralized server to be completed; to NodeJS on Heroku + Redis and preprocessing on SoftLayer (see Catalyst program).

Dedicated to PaaS

The reduction in complexity is significant; we had 8 dedicated servers each running 3 or 4 instances of the API at our hosting partner Hivelocity. Each ran a small suite of software including:

MongoDB instance
log pre-processing service
monitoring service
IIS with api sites

Deploying was done via an FTP script that uploaded new api site versions to all servers. Services were more annoying to deploy but changed infrequently.

MongoDB was a poor choice for temporarily holding log data before it was pre-processed and sent off. It offered a huge speed advantage of just writing to memory initially which meant write requests were “finished” almost instantly which was far superior to common message queues on Windows, but it never reclaimed space left from deleted data which meant the db size would balloon to 100+ gigabytes if it wasn’t compacted regularly.

The advantages of PaaS providers are pretty well known, they all seem quite similar although it’s easiest to have confidence in Heroku and Salesforce since they seem the most mature and have broad technology support.

The main challenges transitioning to PaaS was shaking the mentality that we could run assistive software alongside the website as we did on the dedicated servers. Most platforms provide some sort of background worker threads you can leverage but that means you need to route data and tasks from the web threads through a 3rd party service or server which seems unnecessary.

We eventually settled on a large server at Softlayer running a dozen purpose-specfic Redis instances and some middleware rather than background workers. Heroku doesn’t charge for outbound bandwidth and Softlayer doesn’t charge for inbound which neatly avoided the significant bandwidth involved.

Switching from .NET to NodeJS

Working with JavaScript on the serverside is a mixed experience. On the one hand the lack of formality and boilerplate is liberating. On the other hand there’s no New Relic and no compiler errors which makes everything harder than it needs to be.

There are two main advantages that make NodeJS spectacularly useful for our API.

Background workers in the same thread and memory as the web server
Persistant, shared connections to redis and mongodb (etc)

Background workers

NodeJS has the very useful ability to continue working independently of requests, allowing you to prefetch data and other operations that allow you to terminate a request very early and then finish processing it.

It is particularly advantageous for us to replicate entire MongoDB collections in memory, periodically refreshed, so that entire classes of work had access to current data without having to go an external database or local/shared caching layer.

We collectively save 100s – 1000s of database queries per second using this in:

Game configuration data on our main api
API credentials on our data exporting api
GameVars which developers use to store configuration or other data to hotload into their games
Leaderboard score tables (excluding scores)

The basic model is:

module.exports = function(request, response) {
response.end(cache[“x”]);
}

function refresh() {

// fetch updated data from database, store in cache object
cache[“x”] = “foo”;
setTimeout(refresh, 30000);
}

refresh();

The advantages of this are a single connection (per dyno or instance) to your backend databases instead of per-user, and a very fast local memory cache that always has fresh data.

The caveats are your dataset must be small, and this is operating on the same thread as everything else so you need to be conscious of blocking the thread or doing too-heavy cpu work.

Persistent connections

The other massive benefit NodeJS offers over .NET for our API is persistant database connections. The traditional method of connecting in .NET (etc) is to open your connection, do your operation, after which your connection is returned to a pool to be re-used shortly or expired if it’s no longer needed.

This is very common and until you get to a very high concurrency it will Just Work. At a high concurrency the connection pool can’t re-use the connections fast enough which means it generates new connections that your database servers will have to scale to handle.

At Playtomic we typically have several hundred thousand concurrent game players that are sending event data which needs to be pushed back to our Redis instances in a different datacenter which with .NET would require a massive volume of connections – which is why we ran MongoDB locally on each of our old dedicated servers.

With NodeJS we have a single connection per dyno/instance which is responsible for pushing all the event data that particular dyno receives. It lives outside of the request model something like this:

module.exports = function(request, response) {

var eventdata = “etc”;

redisclient.lpush(“events”, eventdata);

}

The end result

High load:

REQUESTS IN LAST MINUTE

_exceptions: 75 (0.01%)
_failures: 5 (0.00%)
_total: 537,151 (99.99%)
data.custommetric.success: 1,093 (0.20%)
data.levelaveragemetric.success: 2,466 (0.46%)
data.views.success: 105 (0.02%)
events.regular.invalid_or_deleted_game#2: 3,814 (0.71%)
events.regular.success: 527,837 (98.25%)
gamevars.load.success: 1,060 (0.20%)
geoip.lookup.success: 109 (0.02%)
leaderboards.list.success: 457 (0.09%)
leaderboards.save.missing_name_or_source#201: 3 (0.00%)
leaderboards.save.success: 30 (0.01%)
leaderboards.saveandlist.success: 102 (0.02%)
playerlevels.list.success: 62 (0.01%)
playerlevels.load.success: 13 (0.00%)

This data comes from some load monitoring that operates in the background on each instance, pushes counters to Redis where they’re then aggregated and stored in MongoDB, you can see it in action at https://api.playtomic.com/load.html.

There are a few different classes of requests in that data:

Events that check the game configuration from MongoDB, perform a GeoIP lookup (opensourced very fast implementation at https://github.com/benlowry/node-geoip-native), and then push to Redis
GameVars, Leaderboards, Player Levels all check game configuration from MongoDB and then whatever relevant MongoDB database
Data lookups are proxied to a Windows server because of poor NodeJS support for stored procedures

The result is 100,000s of concurrent users causing spectactularly light Redis loads fo 500,000 – 700,000 lpush’s per minute (and being pulled out on the other end):

1 [|| 1.3%] Tasks: 83; 4 running
2 [||||||||||||||||||| 19.0%] Load average: 1.28 1.20 1.19
3 [|||||||||| 9.2%] Uptime: 12 days, 21:48:33
4 [|||||||||||| 11.8%]
5 [|||||||||| 9.9%]
6 [||||||||||||||||| 17.7%]
7 [||||||||||||||| 14.6%]
8 [||||||||||||||||||||| 21.6%]
9 [|||||||||||||||||| 18.2%]
10 [| 0.6%]
11 [ 0.0%]
12 [|||||||||| 9.8%]
13 [|||||||||| 9.3%]
14 [|||||| 4.6%]
15 [|||||||||||||||| 16.6%]
16 [||||||||| 8.0%]
Mem[||||||||||||||| 2009/24020MB]
Swp[ 0/1023MB]

PID USER PRI NI VIRT RES SHR S CPU% MEM% TIME+ Command
12518 redis 20 0 40048 7000 640 S 0.0 0.0 2:21.53 `- /usr/local/bin/redis-server /etc/redis/analytics.conf
12513 redis 20 0 72816 35776 736 S 3.0 0.1 4h06:40 `- /usr/local/bin/redis-server /etc/redis/log7.conf
12508 redis 20 0 72816 35776 736 S 2.0 0.1 4h07:31 `- /usr/local/bin/redis-server /etc/redis/log6.conf
12494 redis 20 0 72816 37824 736 S 1.0 0.2 4h06:08 `- /usr/local/bin/redis-server /etc/redis/log5.conf
12488 redis 20 0 72816 33728 736 S 2.0 0.1 4h09:36 `- /usr/local/bin/redis-server /etc/redis/log4.conf
12481 redis 20 0 72816 35776 736 S 2.0 0.1 4h02:17 `- /usr/local/bin/redis-server /etc/redis/log3.conf
12475 redis 20 0 72816 27588 736 S 2.0 0.1 4h03:07 `- /usr/local/bin/redis-server /etc/redis/log2.conf
12460 redis 20 0 72816 31680 736 S 2.0 0.1 4h10:23 `- /usr/local/bin/redis-server /etc/redis/log1.conf
12440 redis 20 0 72816 33236 736 S 3.0 0.1 4h09:57 `- /usr/local/bin/redis-server /etc/redis/log0.conf
12435 redis 20 0 40048 7044 684 S 0.0 0.0 2:21.71 `- /usr/local/bin/redis-server /etc/redis/redis-servicelog.conf
12429 redis 20 0 395M 115M 736 S 33.0 0.5 60h29:26 `- /usr/local/bin/redis-server /etc/redis/redis-pool.conf
12422 redis 20 0 40048 7096 728 S 0.0 0.0 26:17.38 `- /usr/local/bin/redis-server /etc/redis/redis-load.conf
12409 redis 20 0 40048 6912 560 S 0.0 0.0 2:21.50 `- /usr/local/bin/redis-server /etc/redis/redis-cache.conf

and very light MongoDB loads for 1800 – 2500 crud operations a minute:

insert query update delete getmore command flushes mapped vsize res faults locked % idx miss % qr|qw ar|aw netIn netOut conn time
2 9 5 2 0 8 0 6.67g 14.8g 1.22g 0 0.1 0 0|0 0|0 3k 7k 116 01:11:12
1 1 5 2 0 6 0 6.67g 14.8g 1.22g 0 0.1 0 0|0 0|0 2k 3k 116 01:11:13
0 3 6 2 0 8 0 6.67g 14.8g 1.22g 0 0.2 0 0|0 0|0 3k 6k 114 01:11:14
0 5 5 2 0 12 0 6.67g 14.8g 1.22g 0 0.1 0 0|0 0|0 3k 5k 113 01:11:15
1 9 7 2 0 12 0 6.67g 14.8g 1.22g 0 0.1 0 0|0 0|0 4k 6k 112 01:11:16
1 10 6 2 0 15 0 6.67g 14.8g 1.22g 0 0.1 0 0|0 1|0 4k 22k 111 01:11:17
1 5 6 2 0 11 0 6.67g 14.8g 1.22g 0 0.2 0 0|0 0|0 3k 19k 111 01:11:18
1 5 5 2 0 14 0 6.67g 14.8g 1.22g 0 0.1 0 0|0 0|0 3k 3k 111 01:11:19
1 2 6 2 0 8 0 6.67g 14.8g 1.22g 0 0.2 0 0|0 0|0 3k 2k 111 01:11:20
1 7 5 2 0 9 0 6.67g 14.8g 1.22g 0 0.1 0 0|0 0|0 3k 2k 111 01:11:21
insert query update delete getmore command flushes mapped vsize res faults locked % idx miss % qr|qw ar|aw netIn netOut conn time
2 9 8 2 0 8 0 6.67g 14.8g 1.22g 0 0.2 0 0|0 0|0 4k 5k 111 01:11:22
3 8 7 2 0 9 0 6.67g 14.8g 1.22g 0 0.2 0 0|0 0|0 4k 9k 110 01:11:23
2 6 6 2 0 10 0 6.67g 14.8g 1.22g 0 0.2 0 0|0 0|0 3k 4k 110 01:11:24
2 8 6 2 0 21 0 6.67g 14.8g 1.22g 0 0.2 0 0|0 0|0 4k 93k 112 01:11:25
1 10 7 2 3 16 0 6.67g 14.8g 1.22g 0 0.2 0 0|0 0|0 4k 4m 112 01:11:26
3 15 7 2 3 24 0 6.67g 14.8g 1.23g 0 0.2 0 0|0 0|0 6k 1m 115 01:11:27
1 4 8 2 0 10 0 6.67g 14.8g 1.22g 0 0.2 0 0|0 0|0 4k 2m 115 01:11:28
1 6 7 2 0 14 0 6.67g 14.8g 1.22g 0 0.2 0 0|0 0|0 4k 3k 115 01:11:29
1 3 6 2 0 10 0 6.67g 14.8g 1.22g 0 0.1 0 0|0 0|0 3k 103k 115 01:11:30
2 3 6 2 0 8 0 6.67g 14.8g 1.22g 0 0.2 0 0|0 0|0 3k 12k 114 01:11:31
insert query update delete getmore command flushes mapped vsize res faults locked % idx miss % qr|qw ar|aw netIn netOut conn time
0 12 6 2 0 9 0 6.67g 14.8g 1.22g 0 0.2 0 0|0 0|0 4k 31k 113 01:11:32
2 4 6 2 0 8 0 6.67g 14.8g 1.22g 0 0.1 0 0|0 0|0 3k 9k 111 01:11:33
2 9 6 2 0 7 0 6.67g 14.8g 1.22g 0 0.1 0 0|0 0|0 3k 21k 111 01:11:34
0 8 7 2 0 14 0 6.67g 14.8g 1.22g 0 0.2 0 0|0 0|0 4k 9k 111 01:11:35
1 4 7 2 0 11 0 6.67g 14.8g 1.22g 0 0.2 0 0|0 0|0 3k 5k 109 01:11:36
1 15 6 2 0 19 0 6.67g 14.8g 1.22g 0 0.1 0 0|0 0|0 5k 11k 111 01:11:37
2 17 6 2 0 19 1 6.67g 14.8g 1.22g 0 0.2 0 0|0 0|0 6k 189k 111 01:11:38
1 13 7 2 0 15 0 6.67g 14.8g 1.22g 0 0.2 0 0|0 1|0 5k 42k 110 01:11:39
2 7 5 2 0 77 0 6.67g 14.8g 1.22g 0 0.1 0 0|0 2|0 10k 14k 111 01:11:40
2 10 5 2 0 181 0 6.67g 14.8g 1.22g 0 0.1 0 0|0 0|0 21k 14k 112 01:11:41
insert query update delete getmore command flushes mapped vsize res faults locked % idx miss % qr|qw ar|aw netIn netOut conn time
1 11 5 2 0 12 0 6.67g 14.8g 1.22g 0 0.1 0 0|0 0|0 4k 13k 116 01:11:42
1 11 5 2 1 33 0 6.67g 14.8g 1.22g 0 0.1 0 0|0 3|0 6k 2m 119 01:11:43
0 9 5 2 0 17 0 6.67g 14.8g 1.22g 0 0.1 0 0|0 1|0 5k 42k 121 01:11:44
1 8 7 2 0 25 0 6.67g 14.8g 1.22g 0 0.2 0 0|0 0|0 6k 24k 125 01:11:45

Simpler, Cheaper, Faster: Playtomic's Move from .NET to Node and Heroku

High Scalability

Scaling Playtomic with NodeJS

Dedicated to PaaS