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Trickle Walkthough


In this section we walk through increasingly complex tremor query language ( trickle ) programming examples, introducing key concepts as we progress.

Overview

Trickle is a near real-time stream-based structured query language that builds on the tremor scripting language.

Trickle queries are compiled in pipeline DAGs and replace the yaml pipeline format used in previous versions to describe event processing graphs in the tremor runtime.

The most basic query possible in trickle is

select event from in into out; # A basic passthrough query pipeline

This would be configured/programmed as follows in the more verbose pipeline yaml format:

pipeline:
  - id: main
    interface:
      inputs:
        - in
      outputs:
        - out
    links:
      in: [out]

The event keyword selected the event from the standard input stream in and passes it through unchanged to the standard output stream out.

In tremor's trickle query langauge queries are compiled and optimised and data is streamed though the query. Data can be passed through, transformed, filtered, aggregated, branched or combined to form continuous stream processing algorithms.

Like other event processing languages, trickle inverts the relationship between query and data when compared to normal RDBMS SQL langauges. Instead of running a dynamic query against static in memory or disk persisted data, we compilse and optimise queries and stream near real-time data through each query.

Data can be ingested from the outside world via the 'in' standard stream. Data can be produced to the outside world via the 'out' standard stream. Errors can be processed through a standard 'err' standard stream.

These three primitives are analgous to the stdin, stdout and stderr streams in UNIX-like systems. These can be chained or interconnected via connecting multiple statements together to form a directed acyclic graph.

We can branch inputs using where and having clauses as filters to logically partition streams into independent processing streams.

In the below example we pratition events by their seq_num field. If the number is even, we branch the corresponding events into a stream named evens. If the number is odd, we branch to a stream named odds.

The logical inverse of branching is to unify or union streams together, this operation is also supported via the select operation and is demonstrated below. We combine the evens and odds streams into the standard output stream

# create private intermediate internal streams
create stream evens;
create stream odds;

# branch input into even/odd sequenced events using where clause
select { "even": event } from in where (event.seq_num %2 == 1) into evens;
select { "odd": event } from in where (event.seq_num %2 == 0) into odds;

# combine / union evens and odds into standard out stream
select event from evens into out;
select event from odds into out;

We can test this with a json event using the tremor-query command line tool

{
  "seq_num": 4,
  "value": 10,
  "group": "horse"
}

Assuming the trickle query is stored in a file called evenodd.tricle with the sample event in a file called data.json

$ tremor-query evenodd.trickle -e data.json

The command line tool will repeatedly inject the event -e argument document and we would expect to see output from the tool as follows:

out>>
    1 | {
    2 |   "odd": {
    3 |     "seq_num": 4,
    4 |     "value": 10,
    5 |     "group": "horse"
    6 |   }
    7 | }

Scripts and Operators

The query language is backwards compatible with the legacy pipeline format in that the same operations are available in the old and new syntax. The new structured query syntax is more flexible and powerful, however.

For example, here's the logic for an entire backpressure algorithm that could be introduced as a proxy between two systems:

define generic::backpressure operator bp
with
    timeout = 10000,
    steps = [ 1, 50, 100, 250, 750 ],
end;

create operator bp from bp;
select event from in into bp;
select event from bp into out;

A slightly more complex example that uses both operators and the tremor scripting langauge with the query langauge all together:

define grouper::bucket operator kfc;

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

create operator kfc from kfc;

# where script definition and instance name are the same, we can
# omit the from clause in operator and script 'create' statements
create script categorize;

# Stream ingested data into categorize script
select event from in into categorize;

# Stream scripted events into kfc bucket operator
select event from categorize into kfc;

# Stream bucketed events into out stream
select event from kfc into out;

Operators, in the query langauge as in the pipeline format are defined as <module>::<name> in the context of an operator definition clause. Operators, like script definitions can take arguments.

Definitions in tremor are non-executing. They should be considered as templates or specifications.

In the query langauge, any define clause creates specifications, possibly with arguments for specialization. They are typically incarnated via the create clause. Anything that is createed will form a stream or node in the query graph - these do consume memory and participate in a pipeline query algorithm.

So in the above example, the categorize script and the categorize node have both a definition or specification and an instance node that participates in the graph at runtime. It is often convenient to use the same name where there is only one instance of an operator of a given type.

Building Query Graph Algorithms

Data streams can be branched and combined in the trickle query language via the select statement. The resulting graphs must be acyclic with no direct or indirect looping cycles.

Branching

Branching data streams to multiple streams is performed via select operations

Branch data into 3 different output stream ports

select event from in into out/a;
select event from in into out/b;
select event from in inout out/c;

Branch data into 3 different intermediate streams

create stream a;
create stream b;
create stream c;

select event from in into a;
select event from in into b;
select event from in into c;

Combining

Multiple data streams can also be combined via select operations.

Combine 3 data streams into a single output stream

...

select event from a into out;
select event from b into out;
select event from c inout out;

Combine 3 data stream ports from 1 or many streams into a single output stream

...

select event from a/1 into out;
select event from a/2 into out;
select event from b inout out;

Aggregations

A key feature of the tremor query langauge are aggregations. These are supported with:

  • Aggregate functions - An aggregate function is a function that runs in the context of a temporal window of events, emitting results intermittently
  • Windows - A window is a range of event, clock or data time. There can be many different types of window
  • Tilt Frames - A tilt frame is a chain of compatible windows with decreasing resolution used to reduce memory pressure and preserve relative accuracy of windowed aggregate functions

An example clock-driven tumbling window:

define tumbling window `15secs`
 with
   interval = datetime::with_seconds(15),
end;

select {
    "count": stats::count(), # Aggregate 'count' function
    "min": stats::min(event.value),
    "max": stats::max(event.value),
    "mean": stats::mean(event.value),
    "stdev": stats::stdev(event.value),
    "var": stats::var(event.value),
}
from in[`15secs`] # We apply the window nominally to streams
into out;

To use a window we need to define the window specifications, such as a 15 second clock-based tumbling window called 15secs as above. We can then create instances of these windows at runtime by applying those windows to streams. This is done in the from clause in select expressions.

Wherever windows are applied, aggregate functions can be used. In the abvove example, we are calculating the minimum, maximum, average, standard deviation and variance of a value numeric field in data streaming into the query via the standard input stream.

The query language is not constrained to to clock-driven window definitions. Windows can also be data-drive or fully programmatic.

A more complete example:

select {
    "measurement": event.measurement,
    "tags": patch event.tags of insert "window" => window end,
    "stats": stats::hdr(event.fields[group[2]], [ "0.5", "0.9", "0.99", "0.999" ]),
    "class": group[2],
    "timestamp": win::first(event.timestamp),
}
from in[`10secs`, `1min`, `10min`, `1h`]
where event.measurement == "udp_lb_test"
   or event.measurement == "kafka-proxy.endpoints"
   or event.measurement == "burrow_group"
   or event.measurement == "burrow_partition"
   or event.measurement == "burrow_topic"
group by set(event.measurement, event.tags, each(record::keys(event.fields)))
into normalize;

In the above example we use a single aggregate function called stats::hdr which uses a high dynamic range or HDR Histogram to compute quartile estimates and basic statistics against a number of dynamic grouping fields set by the group clause. A group clause effectively partitions our operation by the group expressions provided by the trickle query programmer. In the example, we're using the field names of the nested 'fields' record on inbound events to compose a component of a group that is also qualified by tags and a measurement name. The field component is used as a numeric input to the histogram aggregate function.

In the from clause, we are using a tilt frame, or a succession of window resolutions over which this aggregate function is producing results. So a 10secs window is emitting on a 10-second repeating basis into a 1min frame. So 6 times per second the state of the 10 second window is merged into the 1min frame. This merge process is performed for each frame in the tilt frame.

The advantage of tilt-frames is that as the target expression is the same for each frame, we can merge across each frame without amplifying error - in short, we get the effect of summarisation without loss of accuracy.

Aggregation Mechanics

The mechanics of aggregation in the query langauge are non-trivial.

A high level non-normative summary follows.

Windowing

Assuming a periodic event delivered every 2 seconds into tremor.

tumbling-event-windows.png

A size based window of size 2 would emit a synthetic output event every 2 events. So the lifespan of a size based window is 2 events, repeated and non-overlapping for tumbling style windows. In the illustration above events 1 and 2 in the first window w0 produce a single synthetic or derivate event a Events 3 and 4 in the second window w1 produce a single synthetic or derivate event b As there is no 6th event in the example illustration, we will never get another synthetic output event

Contrast this with the 10 second or clock-based tumbling window. In tfirst window w0s lifetime we capture all events in the illustration.

Tilt Frames

Assuming a continuous flow of events into tremor...

tilt-frame-mechanics.png

All the synthetic outputs of successive 5 minute windows that fit into a 15 minute interval are merged into the 15 minute window. All the outputs of successive 15 minute intervals that fit into a 1 hour interval are merged into the 1 hour window. By chaining and merging, tremor can optimise ( reduce ) the amount of memory required across the chain when compared to multiple independent windows select expressions. In the case of aggregate functions like stats::hdr`` orstats::dds``` the savings are significant.

If we imagine 1M events per second, that is 300M events every 5 minutes. 900M every 15, 3.6B every hour.

By using tilt frames we can maximally minimize internal memory consumption, whilst reducing the volume of incremental computation ( per event, per frame ), and further whilst preserving relative accuracy for merge-capable aggregate functions.

The converged statistics under merge exhibit the same relative accuracy at a fraction of the computational and memory overhead without the using the tilt-frame mechanism.

Group Mechanics

The group clause in the query language partitions streams before windows and tilt frames are applied. Groups can be set-based, each-based or composites thereof.

Set based grouping

Grouping by set partitions streams by a concatenation of expressions.

select event from in
group by set(event.country, event.region)
into out;

In the example expression we are partitioning into a composite group that is composed of the country and region of each inbound event.

So we expect data of the following form

{ "country": "US", "region": "east", "az": "1", ... }
{ "country": "US", "region": "east", "az": "2", ... }

Each based grouping

Given that our data can be nested, however, our data could be structured differently:

{
  "country": "US",
  regions: {
    "east": [ "1", "2"],
  }
  ...
}
select event from in
group by each(record::keys(event.regions))
into out;

Each field in the nested locations field becomes a component of our set and qualified by country ...

Limitations

There are cases however that currently complex to partitionable with a single sql expression due to limitations with the grouping clause. For example what is we wanted to make availability zones a component of our group partitions?

How would we structure such a query?

{
  "country": "US",
  regions: {
    "east": [ "1", "2"], # AZs by region
  }
  ...
}
create stream by_country_region;

select { "country": event.country, "region": group[0], "azs": event.regions[group[0]] }
from in
group by each(record::keys(event.regions))
into by_country_region;

We can preprocess our inbound stream and collapse our locations sub-record a single level by hoisting the region field to the top level of a synthetic intermediate outbound stream by_country_region.

We can postprocess the intermediate stream by_country_region into a single outbound stream that further extracts and hoists the 'az' dimension

select { "country": event.country, "region": event.region, "az": group[0], }
from by_country_region
group by each(event.azs)
into out;

So, we need 2 select statements to compose a solution where there are multiple nesting levels via successive extraction of group components. The same principle works with more complex grouping requirements.

Once the grouping mechanics are resolved, windowing, aggregation and tilt-frames can be applied to further refine queries.