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Case Study - Data Flow with Tremor

· 6 min read

Happened Before

The simplified architecture of our systems is currently:

old pipeline

Identified Need

The need to route and process data from multiple sources to multiple destinations with one or many potentially overlapping streams of data processing is already evident in the first 2 phases of Tremor’s emergence as a mission critical piece of infrastructure at Wayfair.

Growing adoption and the increased sophistication of processing needs now results in the emergence of event/data flow or pipeline processing by our production users. We summarise multiple phases of evolution here and conflate them into a single case study.

In reality the use case captured in this synopsis composes multiple smaller projects related by the same identified need spanning multiple releases into a single case study.

Required Outcome

Our systems specialists are writing increasingly complex, layered and rich business logic within tremor’s domain specific language tremor-script and are inhibited by our pipeline model which uses the YAML format for describing data flow graphs.

YAML is hard to write, and hard to debug, and the embedded scripting language is an odd fit into the pipeline configuration which is described in YAML.

Preserving the internal pipeline processing and execution mechanisms - replace the verbose YAML syntax with a statement oriented query language that extends the expression oriented scripting language.

This allows hygienic and helpful error reporting with suggested workarounds and fixes, better IDE integration and a more intuitive means to express data flow operations on multiple data streams that the pipeline mechanisms expose to tremor systems application developers.

A structured query-like language suitably captures the directed-acyclic graph nature of data flows but we need to support rich nested JSON-like data structures with leverage of the processing capabilities of the scripting language - whilst opening up new capabilities to extend the processing capabilities through custom operators, embeddable scripting logic and extensions and enhancements to QoS and non-functional primitives.

For the metrics domain - the ability to aggregate tumbling windows of events and to provide multiple aggregation dimensions with flexible grouping policies is a new requirement.

Characteristics

Replace the YAML-based pipeline configuration with a structured query-like language that embeds the scripting language, providing top grade support for processing rich data structures, extensibility through custom operators, and windowing semantics to support multi-resolution event aggregation.

The initial solution was the introduction of the tremor query language.

Solution

The query language not only replaces the YAML-based event flow syntax for describing data flow pipelines and processing - which is useful for every domain using tremor in production at Wayfair - but, it has special considerations for the metrics or other analytic domains that require rich means of grouping and aggregate processing of in-flight streaming data. The embedding of scripting logic allows the traditional domain of ETL and data processing and transformation to be natively supported with a more intuitive syntax.

A rate limiting or traffic shaping example ( common to multiple usage domains ):

# File traffic.trickle
# Very basic traffic shaping algorithm

# Define a bucket grouping operator
define grouper::bucket operator kfc;

# Logic for categorizing events

define script categorize
script
let $rate = 1;
let $class = event.`group`;
{ "event": event, "rate": $rate, "class": $class };
end;

# An instance of the grouper
create operator kfc from kfc;

# An instance of the categorizer
create script categorize;

# Stream ingested events into the categorizer
select event from in into categorize;

# Stream categorized events into the grouper
select event from categorize into kfc;

# Stream categorized and group batched events downstream
select event from kfc into out;

The relatively simple structured query-like language allows script and window definitions to be reused. The define statements do not create operator instances in the data flow graph; they create named reusable definitions that encompass the desired semantics. The create statements create the nodes of the directed acyclic graph. The select statement is a builtin operator that is used for linking nodes in the graph together to form a data flow or event processing graph.

Although the sample logic in the example is a simplified version of what our logging and metrics service teams actually develop and maintain for our production systems - it replaces 1000s of lines of cryptic YAML with hundreds of lines of easy to debug and reason about query code.

An aggregation example from the metrics or analytics domain. This example is a complete but simplified example of a tremor event processing application:

# File: aggregator.trickle
# Aggregate events using a high dynamic range histogram with 10 second, 1 minute, 10 minute
# and 1 hour aggregate summaries.*

select {
"measurement": event.measurement,
"tags": patch
event.tags of insert "window" => window
end,
# Windowed histogram - for median and a selection of quartiles
"stats": aggr::stats::hdr(event.fields[group[2]], [ "0.5","0.9", "0.99", "0.999" ]),
"class": group[2],
"timestamp": aggr::win::first(event.timestamp),
}
# The higher resolution aggregates are merged into lower resolution aggregates to conserve memory
from in[`10secs`, `1min`, `10min`, `1h`]
where event.measurement == "udp_lb"
or event.measurement == "kafka-proxy.endpoints"
or event.measurement == "burrow_group"
or event.measurement == "burrow_partition"
or event.measurement == "burrow_topic"
# The operator maintains a user defined composited set of group partitions.
# Each group maintains its own set of aggregation windows
group by set(event.measurement, event.tags,
each(record::keys(event.fields)))
into normalize
# Discard computed events with a small sample set
having event.stats.count \> 100;

In most real world tremor-based systems - the synthetic events computed in processing pipelines are tailored to conform to the schemas expected by multiple downstream systems:

# File: normalize.trickle
# Prepare computed histogram summaries for downstream systems ( eg: telegraf/influx )
select {
"measurement": event.measurement,
"tags": event.tags,
"fields": {
"count_#{event.class}": event.stats.count,
"min_#{event.class}": event.stats.min,
"max_#{event.class}": event.stats.max,
"mean_#{event.class}": event.stats.mean,
"stdev_#{event.class}": event.stats.stdev,
"var_#{event.class}": event.stats.var,
"p42_#{event.class}": event.stats.percentiles["0.42"],
"p50_#{event.class}": event.stats.percentiles["0.5"],
"p90_#{event.class}": event.stats.percentiles["0.9"],
"p99_#{event.class}": event.stats.percentiles["0.99"],
"p99.9_#{event.class}": event.stats.percentiles["0.999"]
}
}
from normalize
into out;

Compared to the disparate services and software elements that tremor replaces - the query language affords an intuitive and easy to reason about high level domain specific language to configure rich event processing and data enrichment and transformation pipelines - with a minimally terse yet easy to read form.

Conclusion

Tremor’s core mission and mandate includes the efficient declaration of arbitrarily complex directed-acyclic pipeline processing graphs that are memory and compute efficient under the hood whilst preserving transparency or remaining hidden to most of the developers in our organization by continuing to conform to external transports, protocols and service interfaces in the surrounding production infrastructure estate.