blog
2017/08/16
Powerful and modular IO connectors with Splittable DoFn in Apache BeamEugene Kirpichov
One of the most important parts of the Apache Beam ecosystem is its quickly
growing set of connectors that allow Beam pipelines to read and write data to
various data storage systems (“IOs”). Currently, Beam ships over 20 IO
connectors with many more in
active development. As user demands for IO connectors grew, our work on
improving the related Beam APIs (in particular, the Source API) produced an
unexpected result: a generalization of Beam’s most basic primitive, DoFn
.
Note to reader
Hello reader! This blog is a great introduction to Splittable DoFns, but was written while the documentation was catching up. After reading this, you can continue your learning on what Splittable DoFns are and how to implement one in the official Beam Documentation.
Connectors as mini-pipelines
One of the main reasons for this vibrant IO connector ecosystem is that
developing a basic IO is relatively straightforward: many connector
implementations are simply mini-pipelines (composite PTransform
s) made of the
basic Beam ParDo
and GroupByKey
primitives. For example,
ElasticsearchIO.write()
expands
into a single ParDo
with some batching for performance; JdbcIO.read()
expands
into Create.of(query)
, a reshuffle to prevent
fusion,
and ParDo(execute sub-query)
. Some IOs
construct
considerably more complicated pipelines.
This “mini-pipeline” approach is flexible, modular, and generalizes to data
sources that read from a dynamically computed PCollection
of locations, such
as
SpannerIO.readAll()
which reads the results of a PCollection
of queries from Cloud Spanner,
compared to
SpannerIO.read()
which executes a single query. We believe such dynamic data sources are a very
useful capability, often overlooked by other data processing frameworks.
When ParDo and GroupByKey are not enough
Despite the flexibility of ParDo
, GroupByKey
and their derivatives, in some
cases building an efficient IO connector requires extra capabilities.
For example, imagine reading files using the sequence ParDo(filepattern → expand into files)
, ParDo(filename → read records)
, or reading a Kafka topic
using ParDo(topic → list partitions)
, ParDo(topic, partition → read records)
. This approach has two big issues:
In the file example, some files might be much larger than others, so the second
ParDo
may have very long individual@ProcessElement
calls. As a result, the pipeline can suffer from poor performance due to stragglers.In the Kafka example, implementing the second
ParDo
is simply impossible with a regularDoFn
, because it would need to output an infinite number of records per each input elementtopic, partition
(stateful processing comes close, but it has other limitations that make it insufficient for this task).
Beam Source API
Apache Beam historically provides a Source API
(BoundedSource
and
UnboundedSource) which does
not have these limitations and allows development of efficient data sources for
batch and streaming systems. Pipelines use this API via the
Read.from(Source)
built-in PTransform
.
The Source API is largely similar to that of most other data processing
frameworks, and allows the system to read data in parallel using multiple
workers, as well as checkpoint and resume reading from an unbounded data source.
Additionally, the Beam
BoundedSource
API provides advanced features such as progress reporting and dynamic
rebalancing
(which together enable autoscaling), and
UnboundedSource
supports
reporting the source’s watermark and backlog (until SDF, we believed that
“batch” and “streaming” data sources are fundamentally different and thus
require fundamentally different APIs).
Unfortunately, these features come at a price. Coding against the Source API
involves a lot of boilerplate and is error-prone, and it does not compose well
with the rest of the Beam model because a Source
can appear only at the root
of a pipeline. For example:
Using the Source API, it is not possible to read a
PCollection
of filepatterns.A
Source
can not read a side input, or wait on another pipeline step to produce the data.A
Source
can not emit an additional output (for example, records that failed to parse) and so on.
The Source API is not composable even with itself. For example, suppose Alice
implements an unbounded Source
that watches a directory for new matching
files, and Bob implements an unbounded Source
that tails a file. The Source
API does not let them simply chain the sources together and obtain a Source
that returns new records in new log files in a directory (a very common user
request). Instead, such a source would have to be developed mostly from
scratch, and our experience shows that a full-featured monolithic
implementation of such a Source
is incredibly difficult and error-prone.
Another class of issues with the Source
API comes from its strict
bounded/unbounded dichotomy:
It is difficult or impossible to reuse code between seemingly very similar bounded and unbounded sources, for example, the
BoundedSource
that generates a sequence[a, b)
and theUnboundedSource
that generates a sequence[a, inf)
don’t share any code in the Beam Java SDK.It is not clear how to classify the ingestion of a very large and continuously growing dataset. Ingesting its “already available” part seems to require a
BoundedSource
: the runner could benefit from knowing its size, and could perform dynamic rebalancing. However, ingesting the continuously arriving new data seems to require anUnboundedSource
for providing watermarks. From this angle, theSource
API has the same issues as Lambda Architecture.
About two years ago we began thinking about how to address the limitations of
the Source API, and ended up, surprisingly, addressing the limitations of
DoFn
instead.
Enter Splittable DoFn
Splittable DoFn (SDF) is a
generalization of DoFn
that gives it the core capabilities of Source
while
retaining DoFn
’s syntax, flexibility, modularity, and ease of coding. As a
result, it becomes possible to develop more powerful IO connectors than before,
with shorter, simpler, more reusable code.
Note that, unlike Source
, SDF does not have distinct bounded/unbounded APIs,
just as regular DoFn
s don’t: there is only one API, which covers both of these
use cases and anything in between. Thus, SDF closes the final gap in the unified
batch/streaming programming model of Apache Beam.
When reading the explanation of SDF below, keep in mind the running example of a
DoFn
that takes a filename as input and outputs the records in that file.
People familiar with the Source
API may find it useful to think of SDF as a
way to read a PCollection
of sources, treating the source itself as just
another piece of data in the pipeline (this, in fact, was one of the early
design iterations among the work that led to creation of SDF).
The two aspects where Source
has an advantage over a regular DoFn
are:
Splittability: applying a
DoFn
to a single element is monolithic, but reading from aSource
is non-monolithic. The wholeSource
doesn’t have to be read at once; rather, it is read in parts, called bundles. For example, a large file is usually read in several bundles, each reading some sub-range of offsets within the file. Likewise, a Kafka topic (which, of course, can never be read “fully”) is read over an infinite number of bundles, each reading some finite number of elements.Interaction with the runner: runners apply a
DoFn
to a single element as a “black box”, but interact quite richly withSource
.Source
provides the runner with information such as its estimated size (or its generalization, “backlog”), progress through reading the bundle, watermarks etc. The runner uses this information to tune the execution and control the breakdown of theSource
into bundles. For example, a slowly progressing large bundle of a file may be dynamically split by a batch-focused runner before it becomes a straggler, and a latency-focused streaming runner may control how many elements it reads from a source in each bundle to optimize for latency vs. per-bundle overhead.
Non-monolithic element processing with restrictions
Splittable DoFn
supports Source
-like features by allowing the processing of
a single element to be non-monolithic.
The processing of one element by an SDF is decomposed into a (potentially
infinite) number of restrictions, each describing some part of the work to be
done for the whole element. The input to an SDF’s @ProcessElement
call is a
pair of an element and a restriction (compared to a regular DoFn
, which takes
just the element).
Processing of every element starts by creating an initial restriction that
describes the entire work, and the initial restriction is then split further
into sub-restrictions which must logically add up to the original. For example,
for a splittable DoFn
called ReadFn
that takes a filename and outputs
records in the file, the restriction may be a pair of starting and ending byte
offset, and ReadFn
may interpret it as read records whose starting offsets
are in the given range.
The idea of restrictions provides non-monolithic execution - the first
ingredient for parity with Source
. The other ingredient is interaction with
the runner: the runner has access to the restriction of each active
@ProcessElement
call of an SDF, can inquire about the progress of the call,
and most importantly, can split the restriction while it is being processed
(hence the name Splittable DoFn).
Splitting produces a primary and residual restriction that add up to the
original restriction being split: the current @ProcessElement
call keeps
processing the primary, and the residual will be processed by another
@ProcessElement
call. For example, a runner may schedule the residual to be
processed in parallel on another worker.
Splitting of a running @ProcessElement
call has two critically important uses:
- Supporting infinite work per element. A restriction is, in general, not
required to describe a finite amount of work. For example, reading from a Kafka
topic starting from offset 100 can be represented by the
restriction [100, inf). A
@ProcessElement
call processing this entire restriction would, of course, never complete. However, while such a call runs, a runner can split the restriction into a finite primary [100, 150) (letting the current call complete this part) and an infinite residual [150, inf) to be processed later, effectively checkpointing and resuming the call; this can be repeated forever.
- Dynamic rebalancing. When a (typically batch-focused) runner detects that
a
@ProcessElement
call is going to take too long and become a straggler, it can split the restriction in some proportion so that the primary is short enough to not be a straggler, and can schedule the residual in parallel on another worker. For details, see No Shard Left Behind.
Logically, the execution of an SDF on an element works according to the following diagram, where “magic” stands for the runner-specific ability to split the restrictions and schedule processing of residuals.
This diagram emphasizes that splittability is an implementation detail of the
particular DoFn
: a splittable DoFn
still looks like a DoFn<A, B>
to its
user, and can be applied via a ParDo
to a PCollection<A>
producing a
PCollection<B>
.
Which DoFns need to be splittable
Note that decomposition of an element into element/restriction pairs is not
automatic or “magical”: SDF is a new API for authoring a DoFn
, rather than a
new way to execute an existing DoFn
. When making a DoFn
splittable, the
author needs to:
Consider the structure of the work it does for every element.
Come up with a scheme for describing parts of this work using restrictions.
Write code for creating the initial restriction, splitting it, and executing an element/restriction pair.
An overwhelming majority of DoFn
s found in user pipelines do not need to be
made splittable: SDF is an advanced, powerful API, primarily targeting authors
of new IO connectors (though it has interesting non-IO applications as well:
see Non-IO examples).
Execution of a restriction and data consistency
One of the most important parts of the Splittable DoFn
design is related to
how it achieves data consistency while splitting. For example, while the runner
is preparing to split the restriction of an active @ProcessElement
call, how
can it be sure that the call has not concurrently progressed past the point of
splitting?
This is achieved by requiring the processing of a restriction to follow a
certain pattern. We think of a restriction as a sequence of blocks -
elementary indivisible units of work, identified by a position. A
@ProcessElement
call processes the blocks one by one, first claiming the
block’s position to atomically check if it’s still within the range of the
restriction, until the whole restriction is processed.
The diagram below illustrates this for ReadFn
(a splittable DoFn
that reads
Avro files) processing the element foo.avro
with restriction [30, 70)
. This
@ProcessElement
call scans the Avro file for data
blocks
starting from offset 30
and claims the position of each block in this range.
If a block is claimed successfully, then the call outputs all records in this
data block, otherwise, it terminates.
For more details, see Restrictions, blocks and positions in the design proposal document.
Code example
Let us look at some examples of SDF code. The examples use the Beam Java SDK,
which represents splittable
DoFn
s
as part of the flexible annotation-based
DoFn
machinery, and the proposed SDF syntax
for Python.
A splittable
DoFn
is aDoFn
- no new base class needed. Any SDF derives from theDoFn
class and has a@ProcessElement
method.The
@ProcessElement
method takes an additionalRestrictionTracker
parameter that gives access to the current restriction in addition to the current element.An SDF needs to define a
@GetInitialRestriction
method that can create a restriction describing the complete work for a given element.There are several less important optional methods, such as
@SplitRestriction
for pre-splitting the initial restriction into several smaller restrictions, and a few others.
The “Hello World” of SDF is a counter, which takes pairs (x, N) as input and produces pairs (x, 0), (x, 1), …, (x, N-1) as output.
class CountFn<T> extends DoFn<KV<T, Long>, KV<T, Long>> {
@ProcessElement
public void process(ProcessContext c, OffsetRangeTracker tracker) {
for (long i = tracker.currentRestriction().getFrom(); tracker.tryClaim(i); ++i) {
c.output(KV.of(c.element().getKey(), i));
}
}
@GetInitialRestriction
public OffsetRange getInitialRange(KV<T, Long> element) {
return new OffsetRange(0L, element.getValue());
}
}
PCollection<KV<String, Long>> input = …;
PCollection<KV<String, Long>> output = input.apply(
ParDo.of(new CountFn<String>());
This short DoFn
subsumes the functionality of
CountingSource,
but is more flexible: CountingSource
generates only one sequence specified at
pipeline construction time, while this DoFn
can generate a dynamic family of
sequences, one per element in the input collection (it does not matter whether
the input collection is bounded or unbounded).
However, the Source
-specific capabilities of CountingSource
are still
available in CountFn
. For example, if a sequence has a lot of elements, a
batch-focused runner can still apply dynamic rebalancing to it and generate
different subranges of the sequence in parallel by splitting the OffsetRange
.
Likewise, a streaming-focused runner can use the same splitting logic to
checkpoint and resume the generation of the sequence even if it is, for
practical purposes, infinite (for example, when applied to a KV(..., Long.MAX_VALUE)
).
A slightly more complex example is the ReadFn
considered above, which reads
data from Avro files and illustrates the idea of blocks: we provide pseudocode
to illustrate the approach.
class ReadFn extends DoFn<String, AvroRecord> {
@ProcessElement
void process(ProcessContext c, OffsetRangeTracker tracker) {
try (AvroReader reader = Avro.open(filename)) {
// Seek to the first block starting at or after the start offset.
reader.seek(tracker.currentRestriction().getFrom());
while (reader.readNextBlock()) {
// Claim the position of the current Avro block
if (!tracker.tryClaim(reader.currentBlockOffset())) {
// Out of range of the current restriction - we're done.
return;
}
// Emit all records in this block
for (AvroRecord record : reader.currentBlock()) {
c.output(record);
}
}
}
}
@GetInitialRestriction
OffsetRange getInitialRestriction(String filename) {
return new OffsetRange(0, new File(filename).getSize());
}
}
class AvroReader(DoFn):
def process(filename, tracker=DoFn.RestrictionTrackerParam)
with fileio.ChannelFactory.open(filename) as file:
start, stop = tracker.current_restriction()
# Seek to the first block starting at or after the start offset.
file.seek(start)
block = AvroUtils.get_next_block(file)
while block:
# Claim the position of the current Avro block
if not tracker.try_claim(block.start()):
# Out of range of the current restriction - we're done.
return
# Emit all records in this block
for record in block.records():
yield record
block = AvroUtils.get_next_block(file)
def get_initial_restriction(self, filename):
return (0, fileio.ChannelFactory.size_in_bytes(filename))
This hypothetical DoFn
reads records from a single Avro file. Notably missing
is the code for expanding a filepattern: it no longer needs to be part of this
DoFn
! Instead, the SDK includes a
FileIO.matchAll()
transform for expanding a filepattern into a PCollection
of filenames, and
different file format IOs can reuse the same transform, reading the files with
different DoFn
s.
This example demonstrates the benefits of increased modularity allowed by SDF:
FileIO.matchAll()
supports continuous ingestion of new files in streaming
pipelines using .continuously()
, and this functionality becomes automatically
available to various file format IOs. For example,
TextIO.read().watchForNewFiles()
uses FileIO.matchAll()
under the
hood).
Current status
Splittable DoFn
is a major new API, and its delivery and widespread adoption
involves a lot of work in different parts of the Apache Beam ecosystem. Some
of that work is already complete and provides direct benefit to users via new
IO connectors. However, a large amount of work is in progress or planned.
As of August 2017, SDF is available for use in the Beam Java Direct runner and Dataflow Streaming runner, and implementation is in progress in the Flink and Apex runners; see capability matrix for the current status. Support for SDF in the Python SDK is in active development.
Several SDF-based transforms and IO connectors are available for Beam users at
HEAD and will be included in Beam 2.2.0. TextIO
and AvroIO
finally provide
continuous ingestion of files (one of the most frequently requested features)
via .watchForNewFiles()
which is backed by the utility transforms
FileIO.matchAll().continuously()
and the more general
Watch.growthOf()
.
These utility transforms are also independently useful for “power user” use
cases.
To enable more flexible use cases for IOs currently based on the Source API, we will change them to use SDF. This transition is pioneered by TextIO and involves temporarily executing SDF via the Source API to support runners lacking the ability to run SDF directly.
In addition to enabling new IOs, work on SDF has influenced our thinking about other parts of the Beam programming model:
SDF unified the final remaining part of the Beam programming model that was not batch/streaming agnostic (the
Source
API). This led us to consider use cases that cannot be described as purely batch or streaming (for example, ingesting a large amount of historical data and carrying on with more data arriving in real time) and to develop a unified notion of “progress” and “backlog”.The Fn API - the foundation of Beam’s future support for cross-language pipelines - uses SDF as the only concept representing data ingestion.
Implementation of SDF has lead to formalizing pipeline termination semantics and making it consistent between runners.
SDF set a new standard for how modular IO connectors can be, inspiring creation of similar APIs for some non-SDF-based connectors (for example,
SpannerIO.readAll()
and the plannedJdbcIO.readAll()
).
Call to action
Apache Beam thrives on having a large community of contributors. Here are some ways you can get involved in the SDF effort and help make the Beam IO connector ecosystem more modular:
Use the currently available SDF-based IO connectors, provide feedback, file bugs, and suggest or implement improvements.
Propose or develop a new IO connector based on SDF.
Implement or improve support for SDF in your favorite runner.
Subscribe and contribute to the occasional SDF-related discussions on [email protected] (mailing list for Beam users) and [email protected] (mailing list for Beam developers)!