It's unclear whether it's safe to download files within a DoFn.
My DoFn will download a ~20MB file (an ML model) to apply to elements in my pipeline. According to the Beam docs, requirements include serializability and thread-compatibility.
An example (1, 2) is very similar to my DoFn. It demonstrates downloading from a GCP storage bucket (as I'm doing w/ DataflowRunner), but I'm not sure this approach is safe.
Should objects be downloaded to an in-memory bytes buffer instead of downloading to disk, or is there another best practice for this use case? I haven't come across a best practice approach to this pattern yet.
Adding on to this answer.
If your model data is static than you can use below code example to pass your model as side input.
#DoFn to open the model from GCS location
class get_model(beam.DoFn):
def process(self, element):
from apache_beam.io.gcp import gcsio
logging.info('reading model from GCS')
gcs = gcsio.GcsIO()
yield gcs.open(element)
#Pipeline to load pickle file from GCS bucket
model_step = (p
| 'start' >> beam.Create(['gs://somebucket/model'])
| 'load_model' >> beam.ParDo(get_model())
| 'unpickle_model' >> beam.Map(lambda bin: dill.load(bin)))
#DoFn to predict the results.
class predict(beam.DoFn):
def process(self, element, model):
(features, clients) = element
result = model.predict_proba(features)[:, 1]
return [(clients, result)]
#main pipeline to get input and predict results.
_ = (p
| 'get_input' >> #get input based on source and preprocess it.
| 'predict_sk_model' >> beam.ParDo(predict(), beam.pvalue.AsSingleton(model_step))
| 'write' >> #write output based on target.
In case of streaming pipeline if you want to load model again after predefined time, you can check "Slowly-changing lookup cache" pattern here.
If it is a scikit-learn model then you can look at hosting it in Cloud ML Engine and expose it as a REST endpoint. You can then use something like BagState to optimize invocation of models over the network. More details can be found in this link https://beam.apache.org/blog/2017/08/28/timely-processing.html
Related
Use Case
I have some terabytes of US property data to merge. It is spread across two distinct file formats and thousands of files. The source data is split geographically.
I can't find a way to branch a single pipeline into many independent processing flows.
This is especially difficult because the Dataframe API doesn't seem to support a PTransform on a collection of filenames.
Detailed Background
The distribution of files is like this:
StateData - 51 total files (US states + DC)
CountyData - ~2000 total files (county specific, grouped by state)
The ideal pipeline would split into thousands of independent processing steps and complete in minutes.
1 -> 51 (each US state + DC starts processing)
51 -> thousands (each US state then spawns a process that merges the counties, combining at the end for the whole state)
The directory structure is like this:
📂state-data/
|-📜AL.zip
|-📜AK.zip
|-📜...
|-📜WY.zip
📂county-data/
|-📂AL/
|-📜COUNTY1.csv
|-📜COUNTY2.csv
|-📜...
|-📜COUNTY68.csv
|-📂AK/
|-📜...
|-📂.../
|-📂WY/
|-📜...
Sample Data
This is extremely abbreviated, but imagine something like this:
State Level Data - 51 of these (~200 cols wide)
uid
census_plot
flood_zone
abc121
ACVB-1249575
R50
abc122
ACVB-1249575
R50
abc123
ACVB-1249575
R51
abc124
ACVB-1249599
R51
abc125
ACVB-1249599
R50
...
...
...
County Level Data - thousands of these (~300 cols wide)
uid
county
subdivision
tax_id
abc121
04021
Roland Heights
3t4g
abc122
04021
Roland Heights
3g444
abc123
04021
Roland Heights
09udd
...
...
...
...
So we join many county-level to a single state level, and thus have an aggregated, more-complete state-level data set.
Then we aggregate all the states, and we have a national level data set.
Desired Outcome
I can successfully merge one state at a time (many county to one state). I built a pipeline to do that, but the pipeline starts with a single CountyData CSV and a single StateData CSV. The issue is getting to the point where I can load the CountyData and StateData.
In other words:
#
# I need to find a way to generalize this flow to
# dynamically created COUNTY and STATE variables.
#
from apache_beam.dataframe.convert import to_pcollection
from apache_beam.dataframe.io import read_csv
COUNTY = "county-data/AL/*.csv"
STATE = "state-data/AL.zip"
def key_by_uid(elem):
return (elem.uid, elem)
with beam.Pipeline() as p:
county_df = p | read_csv(COUNTY)
county_rows_keyed = to_pcollection(county_df) | beam.Map(key_by_uid)
state_df = pd.read_csv(STATE, compression="zip")
state_rows_keys = to_pcollection(state_df, pipeline=p) | beam.Map(key_by_uid)
merged = ({ "state": state_rows_keys, "county": county_rows_keyed } ) | beam.CoGroupByKey() | beam.Map(merge_logic)
merged | WriteToParquet()
Starting with a list of states
By state, generate filepatterns to the source data
By state, load and merge the filenames
Flatten the output from each state into a US data set.
Write to Parquet file.
with beam.Pipeline(options=pipeline_options) as p:
merged_data = (
p
| beam.Create(cx.STATES)
| "PathsKeyedByState" >> tx.PathsKeyedByState()
# ('AL', {'county-data': 'gs://data/county-data/AL/COUNTY*.csv', 'state-data': 'gs://data/state-data/AL.zip'})
| "MergeSourceDataByState" >> tx.MergeSourceDataByState()
| "MergeAllStateData" >> beam.Flatten()
)
merged_data | "WriteParquet" >> tx.WriteParquet()
The issue I'm having is something like this:
I have two filepatterns in a dictionary, per state. To access those I need to use a DoFn to get at the element.
To communicate the way the data flows, I need access to Pipeline, which is a PTransform. Ex: df = p | read_csv(...)
These appear to be incompatible needs.
Here's an alternative answer.
Read the state data one at a time and flatten them, e.g.
state_dataframe = None
for state in STATES:
df = p | read_csv('/path/to/state')
df['state'] = state
if state_dataframe is None:
state_dataframe = df
else:
state_dataframe = state_dataframe.append(df)
Similarly for county data. Now join them using dataframe operations.
I'm not sure exactly what kind of merging you're doing here, but one way to structure this pipeline might be to have a DoFn that takes the county data in as a filename as an input element (i.e. you'd have a PCollection of county data filenames), opens it up using "normal" Python (e.g. pandas), and then reads the relevant state data in as a side input to do the merge.
Using Apache Beam I am doing computations - and if they succeed I'd like to write the output to one sink, and if there is a failure I'd like to write that to another sink.
Is there any way to handle metadata or content based routing in Apache Beam?
I've used Apache Camel extensively, and so in my mind based on the outcome of a previous transform, I should route a message to a different sink using a router (perhaps determined by a metadata flag I set on the message header). Is there an analogous capability with Apache Beam, or would I instead just have a sequential transform that inspects the PCollection and handles writing to sinks within the transform?
Ideally I'd like this logic (written verbosely for attempted clarity)
result = my_pcollections | 'compute_stuff' >> beam.Map(lambda (pcollection): my_compute_func(pcollection))
result | ([success_failure_router]
| 'sucess_sink' >> beam.io.WriteToText('/path/to/file')
| 'failure_sink' >> beam.io.WriteStringsToPubSub('mytopic'))
However.. I suspect the 'Beam' way of handling this is
result = my_pcollections | 'compute_stuff' >> beam.Map(lambda (pcollection): my_compute_func(pcollection))
result | 'write_results_appropriately' >> write_results_appropriately(result))
...
def write_results_appropriately(result):
if result == ..:
# success, write to file
else:
# failure, write to topic
Thanks,
Kevin
High-level:
I am not sure of specifics of the Python API in this case, but from high level it looks like this:
par-dos support multiple outputs;
outputs are identified by the tag you give them (e.g. "correct-elements", "invalid-elements");
in your main par-do you write to multiple outputs choosing the output using your criteria;
each output is represented by a separate PCollection;
then you get the separate PCollections representing the tagged outputs from your par-do;
then apply different sinks to each of the tagged PCollections;
In detail see the section
https://beam.apache.org/documentation/programming-guide/#additional-outputs
After using TextIO.read to get a PCollection<String> of the individual lines, is it possible to then use some kind of combine transform to into batches (groups of 25 for example)? So the return type would end up looking something like: PCollection<String, List<String>>. It looks like it should be possible using some kind of CombineFn, but the API is a little arcane to me still.
The context here is I'm reading CSV files (potentially very very large), parsing + processing the lines and turning them into JSON, and then calling a REST API... but I don't want to hit the REST API for each line individually because the REST API supports multiple items at a time (up to 1000, so not the whole batch).
I guess you can do some simple batching like below (using stateful API). The state you want to maintain in BatchingFn is the current buffer of lines or self._lines. Sorry I did it in python (not familiar with Java API)
from apache_beam.transforms import DoFn
from apache_beam.transforms import ParDo
MY_BATCH_SIZE = 512
class BatchingFn(DoFn):
def __init__(self, batch_size=100):
self._batch_size = batch_size
def start_bundle(self):
# buffer for string of lines
self._lines = []
def process(self, element):
# Input element is a string (representing a CSV line)
self._lines.append(element)
if len(_lines) >= self._batch_size:
self._flush_batch()
def finish_bundle(self):
# takes care of the unflushed buffer before finishing
if self._lines:
self._flush_batch()
def _flush_batch(self):
#### Do your REST API call here with self._lines
# .....
# Clear the buffer.
self._lines = []
# pcoll is your PCollection of lines.
(pcoll | 'Call Rest API with batch data' >> ParDo(BatchingFn(MY_BATCH_SIZE)))
Regarding using Data-driven triggers, you can refer to Batch PCollection in Beam/Dataflow.
Is there any way to check if a PCollection is empty?
I haven't found anything relevant in the documentation of Dataflow and Apache Beam.
You didn't specify which SDK you're using, so I assumed Python. The code is easily portable to Java.
You can apply global counting of elements and then map numeric value to boolean by applying simple comparison. You will be able to side-input this value using pvalue.AsSingleton function, like this:
import apache_beam as beam
from apache_beam import pvalue
is_empty_check = (your_pcollection
| "Count" >> beam.combiners.Count.Globally()
| "Is empty?" >> beam.Map(lambda n: n == 0)
)
another_pipeline_branch = (
p
| beam.Map(do_something, is_empty=pvalue.AsSingleton(is_empty_check))
)
Usage of the side input is the following:
def do_something(element, is_empty):
if is_empty:
# yes
else:
# no
There is no way to check size of the PCollection without applying a PTransform on it (such as Count.globally() or Combine.combineFn()) because PCollection is not like a typical Collection in Java SDK or so.
It is an abstraction of bounded or unbounded collection of data where data is fed into the collection for an operation being applied on it (e.g. PTransform). Also it is parallelized (as the P at the beginning of the class suggest).
Therefore you need a mechanism to get counts of elements from each worker/node and combine them to get a value. Whether it is 0 or n can not be known until the end of that transformation.
I have an Apache Beam program that processes the file that comes in a GCS bucket and dumps the data in some BigQuery table. Depending on the file, I want to set the truncate or append operation. Can this be made dynamic or configurable?
Thank You.
I assume that when you say "depending on file", you have some information about the file (to recognize which when to use WRITE_TRUNCATE and WRITE_APPEND) in your pipeline.
Easiest thing to do will be to split the input you're passing into BigQuery into two PCollections (by filtering) and pass each of them into appropriate BigQuery sink (one with WRITE_TRUNCATE and one with WRITE_APPEND).
You didn't mention if you use Java or Python, the pseudocode below is for Python, but it could be easily ported to Java SDK
files = (pipeline
| 'Read files' >> beam.io.Read(Your_GCS_Source())
)
files_to_truncate = (files
| beam.Filter(lambda file: filter_for_files_to_truncate())
| beam.io.Write(beam.io.BigQuerySink(output_table, schema=output_schema, create_disposition=create_disposition, write_disposition=beam.io.BigQueryDisposition.WRITE_TRUNCATE))
)
files_to_append = (files
| beam.Filter(lambda file: filter_for_files_to_append())
| beam.io.Write(beam.io.BigQuerySink(output_table, schema=output_schema, create_disposition=create_disposition, write_disposition=beam.io.BigQueryDisposition.WRITE_APPEND))
)