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Case Study - Traffic Shaping

· 5 min read

Happened Before

This is our origin story. The simplified high level architecture of our logging systems at the time of tremor’s birth is:

old pipeline

Identified Need

Enable load shedding and consistent capacity management at production scale of the logging and metrics firehoses within Wayfair’s production, staging and development environments; even when our live production working set exceeds planned capacity, but especially when catastrophic failure of infrastructure overwhelms our storage tier.

Required Outcome

Our network operations center, system reliability and service organization engineers should never be in a position where in-flight mission-critical events are unavailable or lagging ( seconds or even minutes ) the live working set state of our production systems.

Stale operational events direct limited resources under time constraints and production service level objective pressures to curing or tending to the wrong problems, at the wrong times and lead to missed opportunities for early and cheap resolution.


It is understood that, if a critical system ( such as a production database ) fails - query failures won’t have a single point of origin in the logs - whether over time, or across application or business units. When under extreme load beyond capacity - only a subset of the firehose is required for in-flight diagnostics of our production estate.

In-flight categorization, classification & rate limiting enables just-in-time traffic shaping, load-shedding and bending critical infrastructure to available capacity even as capacity changes in planned or surprising ways.

It is understood that, regardless of the system state - that some logs from a subset of systems, applications or services are relatively more important than others. This is even more true when our systems and services are under extraordinary strain due to unplanned failures and outages. Unplanned failures and outages are expected at Wayfair production scale.

The relative priority of classified and categorized events is known and the relative priority is subject to change over time, as are the event classifications and categorizations.

It is likely impossible to convince thousands of engineers in hundreds of teams across our technology organization to stop using JSON format in the short, medium and possibly even longer term.

Snot. It’s a JSON rock’n’roll world. I guess we’ll just have to deal with it


This works fine 99% of the time - but under extreme load it just does not work at our scale and the impact to our network operations and service reliability and technical communities is severe at the exact moments when we are at peak trading periods and impact to the business is highest - a perfect storm.

old pipeline

In our ELK ( ElasticSearch, Logstash, Kibana ) based logging and Influx ( Telegraf, Chronograf, InfluxDB ) based metrics environments there are limited means to dynamically tune the production system to accommodate highly variant working sets. As we are an online eCommerce system we often go through sustained periods of nominal activity followed by insane periods of extreme peak activity - often hitting peaks we’ve never before experienced.

We do however, have a very experienced infrastructure and platform engineering organization that are deeply attuned to our working set and changing business and operational needs.

The initial version of tremor simply preserved the overall pipeline:

  • Applications in a number of programming languages generate logs
  • A Logstash sidecar receives, normalizes and forwards logs to elasticsearch
  • ElasticSearch indexes logs for display by SRE’s and developers in Kibana

The processing stages remained unchanged ( and largely serviced by Logstash ):

  • Enrich raw logs with business unit, organization, application and environment metadata
  • Normalize schemas - map logs to a common structure and well known field names
  • Known unknowns - isolate user defined fields and extensions to a well known slush field

But added an extra processing stage, a tremor cluster that added back-pressure detection and load-adaptive rate-limiting based on in-flight analysis of the business events in the firehose based on real-time categorization and classification of those event streams.

Now, in the ( significantly less than 1% ) of time when our systems are over-capacity or experiencing catastrophic unplanned events - tremor can discard events based rate limits and event classifications that can be tuned by our SRE, NOC and systems architects and developers.


Tremor’s core mission and mandate started out with, and continues to be the non-functional quality of service of mission critical at scale event based systems. Providing system operators with first class means to shape and tune capacity, load and production countermeasures of the production firehose without compromising the business with system downtime is the core requirement.

The most effective way to achieve this in a highly fluid and ever changing environment is what has yielded the tremor project as it became to be - a general purpose event processing system designed for at scale distributed mission critical systems.

When at or over capacity systems no longer bend to the business - tremor is how we inject a little elasticity into our production quality of service - enriching our systems and services with just-in-time load-adaptive bendability.

new pipeline