A simple way to get more value from metrics | Patreon

We spent one day1 building a system that immediately found a mid 7 figure optimization (which ended up shipping). In the first year, we shipped mid 8 figures per year worth of cost savings as a result. The key feature this system introduces is the ability to query metrics data across all hosts and all services and over any period of time (since inception), so we've called it LongTermMetrics (LTM) internally since I like boring, descriptive, names.

This got started when I was looking for a starter project that would both help me understand the Twitter infra stack and also have some easily quantifiable value. Andy Wilcox suggested looking at JVM survivor space utilization for some large services. If you're not familiar with what survivor space is, you can think of it as a configurable, fixed-size buffer, in the JVM (at least if you use the GC algorithm that's default at Twitter). At the time, if you looked at a random large services, you'd usually find that either:

  1. The buffer was too small, resulting in poor performance, sometimes catastrophically poor when under high load.
  2. The buffer was too large, resulting in wasted memory, i.e., wasted money.

But instead of looking at random services, there's no fundamental reason that we shouldn't be able to query all services and get a list of which services have room for improvement in their configuration, sorted by performance degradation or cost savings. And if we write that query for JVM survivor space, this also goes for other configuration parameters (e.g., other JVM parameters, CPU quota, memory quota, etc.). Writing a query that worked for all the services turned out to be a little more difficult than I was hoping due to a combination of data consistency and performance issues. Data consistency issues included things like:

Our metrics database, MetricsDB, was specialized to handle monitoring, dashboards, alerts, etc. and didn't support general queries. That's totally reasonable, since monitoring and dashboards are lower on Maslow's hierarchy of observability needs than general metrics analytics. In backchannel discussions from folks at other companies, the entire set of systems around MetricsDB seems to have solved a lot of the problems that plauge people at other companies with similar scale, but the specialization meant that we couldn't run arbitrary SQL queries against metrics in MetricsDB.

Another way to query the data is to use the copy that gets written to HDFS in Parquet format, which allows people to run arbitrary SQL queries (as well as write Scalding (MapReduce) jobs that consume the data).

Unfortunately, due to the number of metric names, the data on HDFS can't be stored in a columnar format with one column per name -- Presto gets unhappy if you feed it too many columns and we have enough different metrics that we're well beyond that limit. If you don't use a columnar format (and don't apply any other tricks), you end up reading a lot of data for any non-trivial query. The result was that you couldn't run any non-trivial query (or even many trivial queries) across all services or all hosts without having it time out. We don't have similar timeouts for Scalding, but Scalding performance is much worse and a simple Scalding query against a day's worth of metrics will usually take between three and twenty hours, depending on cluster load, making it unreasonable to use Scalding for any kind of exploratory data analysis.

Given the data infrastructure that already existed, an easy way to solve both of these problems was to write a Scalding job to store the 0.1% to 0.01% of metrics data that we care about for performance or capacity related queries and re-write it into a columnar format. I would guess that at least 90% of metrics are things that almost no one will want to look at in almost any circumstance, and of the metrics anyone really cares about, the vast majority aren't performance related. A happy side effect of this is that since such a small fraction of the data is relevant, it's cheap to store it indefinitely. The standard metrics data dump is deleted after a few weeks because it's large enough that it would be prohibitively expensive to store it indefinitely; a longer metrics memory will be useful for capacity planning or other analyses that prefer to have historical data.

The data we're saving includes (but isn't limited to) the following things for each shard of each service:

And for each host:

For things that we know change very infrequently (like host NIC speed), we store these daily, but most of these are stored at the same frequency and granularity that our other metrics is stored for. In some cases, this is obviously wasteful (e.g., for JVM tenuring threshold, which is typically identical across every shard of a service and rarely changes), but this was the easiest way to handle this given the infra we have around metrics.

Although the impetus for this project was figuring out which services were under or over configured for JVM survivor space, it started with GC and container metrics since those were very obvious things to look at and we've been incrementally adding other metrics since then. To get an idea of the kinds of things we can query for and how simple queries are if you know a bit of SQL, here are some examples:

Very High p90 JVM Survivor Space

This is part of the original goal of finding under/over-provisioned services. Any service with a very high p90 JVM survivor space utilization is probably under-provisioned on survivor space. Similarly, anything with a very low p99 or p999 JVM survivor space utilization when under peak load is probably overprovisioned (query not displayed here, but we can scope the query to times of high load).

A Presto query for very high p90 survivor space across all services is:

with results as (
  select servicename,
    approx_distinct(source, 0.1) as approx_sources, -- number of shards for the service
    -- real query uses [coalesce and nullif](https://prestodb.io/docs/current/functions/conditional.html) to handle edge cases, omitted for brevity
    approx_percentile(jvmSurvivorUsed / jvmSurvivorMax, 0.90) as p90_used,
    approx_percentile(jvmSurvivorUsed / jvmSurvivorMax, 0.50) as p50_used,
  from ltm_service 
  where ds >= '2020-02-01' and ds <= '2020-02-28'
  group by servicename)
select * from results
where approx_sources > 100
order by p90_used desc

Rather than having to look through a bunch of dashboards, we can just get a list and then send diffs with config changes to the appropriate teams or write a script that takes the output of the query and automatically writes the diff. The above query provides a pattern for any basic utilization numbers or rates; you could look at memory usage, new or old gen GC frequency, etc., with similar queries. In one case, we found a service that was wasting enough RAM to pay my salary for a decade.

I've been moving away from using thresholds against simple percentiles to find issues, but I'm presenting this query because this is a thing people commonly want to do that's useful and I can write this without having to spend a lot of space explain why it's a reasonable thing to do; what I prefer to do instead is out of scope of this post and probably deserves its own post.

Network utilization

The above query was over all services, but we can also query across hosts. In addition, we can do queries that join against properties of the host, feature flags, etc.

Using one set of queries, we were able to determine that we had a significant number of services running up against network limits even though host-level network utilization was low. The compute platform team then did a gradual rollout of a change to network caps, which we monitored with queries like the one below to determine that we weren't see any performance degradation (theoretically possible if increasing network caps caused hosts or switches to hit network limits).

With the network change, we were able to observe, smaller queue depths, smaller queue size (in bytes), fewer packet drops, etc.

The query below only shows queue depths for brevity; adding all of the quantities mentioned is just a matter of typing more names in.

The general thing we can do is, for any particular rollout of a platform or service-level feature, we can see the impact on real services.

with rolled as (
   -- rollout was fixed for all hosts during the time period, can pick an arbitrary element from the time period
   arbitrary(element_at(misc, 'egress_rate_limit_increase')) as rollout,
 from ltm_deploys
 where ds = '2019-10-10'
 and zone = 'foo'
 group by ipAddress
), host_info as(
   arbitrary(nicSpeed) as nicSpeed,
 from ltm_host
 where ds = '2019-10-10'
 and zone = 'foo'
 group by ipAddress
), host_rolled as (
 from rolled
 join host_info on rolled.ipAddress = host_info.ipAddress
), container_metrics as (
 from ltm_container
 where ds >= '2019-10-10' and ds <= '2019-10-14'
 and zone = 'foo'
 approx_percentile(netTxQlen, 1, 0.999, 0.0001) as p999_qlen,
 approx_percentile(netTxQlen, 1, 0.99, 0.001) as p99_qlen,
 approx_percentile(netTxQlen, 0.9) as p90_qlen,
 approx_percentile(netTxQlen, 0.68) as p68_qlen,
 count(*) as cnt
from container_metrics
join host_rolled on host_rolled.hostId = container_metrics.hostId
group by service, nicSpeed, rollout

Other questions that became easy to answer

Design decisions

LTM is about as boring a system as is possible. Every design decision falls out of taking the path of least resistance.

Boring technology

I think writing about systems like this, that are just boring work is really underrated. A disproportionate number of posts and talks I read are about systems using hot technologies. I don't have anything against hot new technologies, but a lot of useful work comes from plugging boring technologies together and doing the obvious thing. Since posts and talks about boring work are relatively rare, I think writing up something like this is more useful than it has any right to be.

For example, a couple years ago, at a local meetup that Matt Singer organizes for companies in our size class to discuss infrastructure (basically, companies that are smaller than FB/Amazon/Google) I asked if anyone was doing something similar to what we'd just done. No one who was there was (or not who'd admit to it, anyway), and engineers from two different companies expressed shock that we could store so much data, and not just the average per time period, but some histogram information as well. This work is too straightforward and obvious to be novel, I'm sure people have built analogous systems in many places. It's literally just storing metrics data on HDFS (or, if you prefer a more general term, a data lake) indefinitely in a format that allows interactive queries.

If you do the math on the cost of metrics data storage for a project like this in a company in our size class, the storage cost is basically a rounding error. We've shipped individual diffs that easily pay for the storage cost for decades. I don't think there's any reason storing a few years or even a decade worth of metrics should be shocking when people deploy analytics and observability tools that cost much more all the time. But it turns out this was surprising, in part because people don't write up work this boring.

An unrelated example is that, a while back, I ran into someone at a similarly sized company who wanted to get similar insights out of their metrics data. Instead of starting with something that would take a day, like this project, they started with deep learning. While I think there's value in applying ML and/or stats to infra metrics, they turned a project that could return significant value to the company after a couple of person-days into a project that took person-years. And if you're only going to either apply simple heuristics guided by someone with infra experience and simple statistical models or naively apply deep learning, I think the former has much higher ROI. Applying both sophisticated stats/ML and practitioner guided heuristics together can get you better results than either alone, but I think it makes a lot more sense to start with the simple project that takes a day to build out and maybe another day or two to start to apply than to start with a project that takes months or years to build out and start to apply. But there are a lot of biases towards doing the larger project: it makes a better resume item (deep learning!), in many places, it makes a better promo case, and people are more likely to give a talk or write up a blog post on the cool system that uses deep learning.

The above discusses why writing up work is valuable for the industry in general. We covered why writing up work is valuable to the company doing the write-up in a previous post, so I'm not going to re-hash that here.

Appendix: stuff I screwed up

I think it's unfortunate that you don't get to hear about the downsides of systems without backchannel chatter, so here are things I did that are pretty obvious mistakes in retrospect. I'll add to this when something else becomes obvious in retrospect.

These are the kind of thing you expect when you crank out something quickly and don't think it through enough. The last item is trivial to fix and not much of a problem since the ubiquitous use of IDEs at Twitter means that basically anyone who would be impacted will have their IDE supply the correct capitalization for them.

The first item is more problematic, both in that it could actually cause incorrect analyses and in that fixing it will require doing a migration of all the data we have. My guess is that, at this point, this will be half a week to a week of work, which I could've easily avoided by spending thirty more seconds thinking through what I was doing.

The second item is somewhere in between. Between the first and second items, I think I've probably signed up for roughly double the amount of direct work on this system (so, not including time spent on data analysis on data in the system, just the time spent to build the system) for essentially no benefit.

Thanks to Leah Hanson, Andy Wilcox, Lifan Zeng, and Matej Stuchlik for comments/corrections/discussion

  1. The actual work involved was about a day's work, but it was done over a week since I had to learn Scala as well as Scalding and the general Twitter stack, the metrics stack, etc.

    One day is also just an estimate for the work for the initial data sets. Since then, I've done probably a couple more weeks of work and Wesley Aptekar-Cassels and Kunal Trivedi have probably put in another week or two of time. The opertional cost is probably something like 1-2 days of my time per month (on average), bringing the total cost to on the order a month or two.

    I'm also not counting time spent using the dataset, or time spent debugging issues, which will include a lot of time that I can only roughly guess at, e.g., when the compute platform team changed the network egress limits as a result of some data analysis that took about an hour, that exposed a latent mesos bug that probably cost a day of Ilya Pronin's time, David Mackey has spent a fair amount of time tracking down weird issues where the data shows something odd is going on, but we don't know what is, etc. If you wanted to fully account for time spent on work that came out of some data analysis on the data sets discussed in the post, I suspect, between service-level teams, plus platform-level teams like our JVM, OS, and HW teams, we're probably at roughly 1 person-year of time.

    But, because the initial work it took to create a working and useful system was a day plus time spent working on orientation material and the system returned seven figures, it's been very easy to justify all of this additional time spent, which probably wouldn't have been the case if a year of up-front work was required. Most of the rest of the time isn't the kind of thing that's usually "charged" on roadmap reviews on creating a system (time spent by users, operational overhead), but perhaps the ongoing operational cost shlould be "charged" when creating the system (I don't think it makes sense to "charge" time spent by users to the system since, the more useful a system is, the more time users will spend using it, that doesn't really seem like a cost).

    There'a also been work to build tools on top of this, Kunal Trivedi has spent a fair amount of time building a layer on top of this to make the presentation more user friendly than SQL queries, which could arguably be charged to this project.