Prepared-to-go pattern information pipelines with Dataflow | by Netflix Expertise Weblog | Dec, 2022
by Jasmine Omeke, Obi-Ike Nwoke, Olek Gorajek
This publish is for all information practitioners, who’re interested by studying about bootstrapping, standardization and automation of batch information pipelines at Netflix.
Chances are you’ll keep in mind Dataflow from the publish we wrote final 12 months titled Information pipeline asset administration with Dataflow. That article was a deep dive into one of many extra technical features of Dataflow and didn’t correctly introduce this instrument within the first place. This time we’ll attempt to give justice to the intro after which we’ll give attention to one of many very first options Dataflow got here with. That characteristic is known as pattern workflows, however earlier than we begin in let’s have a fast have a look at Dataflow normally.
Dataflow
Dataflow is a command line utility constructed to enhance expertise and to streamline the info pipeline improvement at Netflix. Try this excessive degree Dataflow assist command output under:
$ dataflow --help
Utilization: dataflow [OPTIONS] COMMAND [ARGS]...Choices:
--docker-image TEXT Url of the docker picture to run in.
--run-in-docker Run dataflow in a docker container.
-v, --verbose Permits verbose mode.
--version Present the model and exit.
--help Present this message and exit.
Instructions:
migration Handle schema migration.
mock Generate or validate mock datasets.
mission Handle a Dataflow mission.
pattern Generate totally useful pattern workflows.
As you may see, the Dataflow CLI is split into 4 primary topic areas (or instructions). Essentially the most generally used one is dataflow mission, which helps people in managing their information pipeline repositories by way of creation, testing, deployment and few different actions.
The dataflow migration command is a particular characteristic, developed single handedly by Stephen Huenneke, to totally automate the communication and monitoring of a knowledge warehouse desk adjustments. Due to the Netflix inside lineage system (constructed by Girish Lingappa) Dataflow migration can then show you how to determine downstream utilization of the desk in query. And eventually it will probably show you how to craft a message to all of the homeowners of those dependencies. After your migration has began Dataflow may also maintain observe of its progress and show you how to talk with the downstream customers.
Dataflow mock command is one other standalone characteristic. It allows you to create YAML formatted mock information information based mostly on chosen tables, columns and some rows of information from the Netflix information warehouse. Its primary objective is to allow simple unit testing of your information pipelines, however it will probably technically be utilized in some other conditions as a readable information format for small information units.
All of the above instructions are very more likely to be described in separate future weblog posts, however proper now let’s give attention to the dataflow pattern command.
Dataflow pattern workflows is a set of templates anybody can use to bootstrap their information pipeline mission. And by “pattern” we imply “an instance”, like meals samples in your native grocery retailer. One of many primary causes this characteristic exists is rather like with meals samples, to present you “a style” of the manufacturing high quality ETL code that you might encounter contained in the Netflix information ecosystem.
All of the code you get with the Dataflow pattern workflows is totally useful, adjusted to your setting and remoted from different pattern workflows that others generated. This pipeline is protected to run the second it reveals up in your listing. It should, not solely, construct a pleasant instance mixture desk and fill it up with actual information, however it’s going to additionally current you with an entire set of beneficial parts:
- clear DDL code,
- correct desk metadata settings,
- transformation job (in a language of alternative) wrapped in an non-obligatory WAP (Write, Audit, Publish) sample,
- pattern set of information audits for the generated information,
- and a completely useful unit take a look at to your transformation logic.
And final, however not least, these pattern workflows are being examined constantly as a part of the Dataflow code change protocol, so you may make sure that what you get is working. That is one strategy to construct belief with our inside person base.
Subsequent, let’s take a look on the precise enterprise logic of those pattern workflows.
Enterprise Logic
There are a number of variants of the pattern workflow you will get from Dataflow, however all of them share the identical enterprise logic. This was a acutely aware choice with a purpose to clearly illustrate the distinction between varied languages during which your ETL might be written in. Clearly not all instruments are made with the identical use case in thoughts, so we’re planning so as to add extra code samples for different (than classical batch ETL) information processing functions, e.g. Machine Studying mannequin constructing and scoring.
The instance enterprise logic we use in our template computes the highest hundred films/reveals in each nation the place Netflix operates every day. This isn’t an precise manufacturing pipeline working at Netflix, as a result of it’s a extremely simplified code but it surely serves effectively the aim of illustrating a batch ETL job with varied transformation phases. Let’s evaluation the transformation steps under.
Step 1: every day, incrementally, sum up all viewing time of all films and reveals in each nation
WITH STEP_1 AS (
SELECT
title_id
, country_code
, SUM(view_hours) AS view_hours
FROM some_db.source_table
WHERE playback_date = CURRENT_DATE
GROUP BY
title_id
, country_code
)
Step 2: rank all titles from most watched to least in each county
WITH STEP_2 AS (
SELECT
title_id
, country_code
, view_hours
, RANK() OVER (
PARTITION BY country_code
ORDER BY view_hours DESC
) AS title_rank
FROM STEP_1
)
Step 3: filter all titles to the highest 100
WITH STEP_3 AS (
SELECT
title_id
, country_code
, view_hours
, title_rank
FROM STEP_2
WHERE title_rank <= 100
)
Now, utilizing the above easy 3-step transformation, we’ll produce information that may be written to the next Iceberg desk:
CREATE TABLE IF NOT EXISTS $TARGET_DB.dataflow_sample_results (
title_id INT COMMENT "Title ID of the film or present."
, country_code STRING COMMENT "Nation code of the playback session."
, title_rank INT COMMENT "Rank of a given title in a given nation."
, view_hours DOUBLE COMMENT "Complete viewing hours of a given title in a given nation."
)
COMMENT
"Instance dataset delivered to you by Dataflow. For extra data on this
and different examples please go to the Dataflow documentation web page."
PARTITIONED BY (
date DATE COMMENT "Playback date."
)
STORED AS ICEBERG;
As you may infer from the above desk construction we’re going to load about 19,000 rows into this desk every day. And they’ll look one thing like this:
sql> SELECT * FROM foo.dataflow_sample_results
WHERE date = 20220101 and country_code = 'US'
ORDER BY title_rank LIMIT 5;title_id | country_code | title_rank | view_hours | date
----------+--------------+------------+------------+----------
11111111 | US | 1 | 123 | 20220101
44444444 | US | 2 | 111 | 20220101
33333333 | US | 3 | 98 | 20220101
55555555 | US | 4 | 55 | 20220101
22222222 | US | 5 | 11 | 20220101
(5 rows)
With the enterprise logic out of the way in which, we are able to now begin speaking in regards to the parts, or the boiler-plate, of our pattern workflows.
Parts
Let’s take a look at the commonest workflow parts that we use at Netflix. These parts could not match into each ETL use case, however are used usually sufficient to be included in each template (or pattern workflow). The workflow writer, in spite of everything, has the ultimate phrase on whether or not they wish to use all of those patterns or maintain just some. Both manner they’re right here to begin with, able to go, if wanted.
Workflow Definitions
Beneath you may see a typical file construction of a pattern workflow package deal written in SparkSQL.
.
├── backfill.sch.yaml
├── every day.sch.yaml
├── primary.sch.yaml
├── ddl
│ └── dataflow_sparksql_sample.sql
└── src
├── mocks
│ ├── dataflow_pyspark_sample.yaml
│ └── some_db.source_table.yaml
├── sparksql_write.sql
└── test_sparksql_write.py
Above bolded information outline a sequence of steps (a.okay.a. jobs) their cadence, dependencies, and the sequence during which they need to be executed.
That is a method we are able to tie parts collectively right into a cohesive workflow. In each pattern workflow package deal there are three workflow definition information that work collectively to offer versatile performance. The pattern workflow code assumes a every day execution sample, however it is extremely simple to regulate them to run at completely different cadence. For the workflow orchestration we use Netflix homegrown Maestro scheduler.
The primary workflow definition file holds the logic of a single run, on this case one day-worth of information. This logic consists of the next components: DDL code, desk metadata data, information transformation and some audit steps. It’s designed to run for a single date, and meant to be known as from the every day or backfill workflows. This primary workflow can be known as manually throughout improvement with arbitrary run-time parameters to get a really feel for the workflow in motion.
The every day workflow executes the primary one every day for the predefined variety of earlier days. That is typically needed for the aim of catching up on some late arriving information. That is the place we outline a set off schedule, notifications schemes, and replace the “excessive water mark” timestamps on our goal desk.
The backfill workflow executes the primary for a specified vary of days. That is helpful for restating information, most frequently due to a metamorphosis logic change, however typically as a response to upstream information updates.
DDL
Usually, step one in a knowledge pipeline is to outline the goal desk construction and column metadata through a DDL assertion. We perceive that some people select to have their output schema be an implicit results of the rework code itself, however the express assertion of the output schema shouldn’t be solely helpful for including desk (and column) degree feedback, but in addition serves as one strategy to validate the rework logic.
.
├── backfill.sch.yaml
├── every day.sch.yaml
├── primary.sch.yaml
├── ddl
│ └── dataflow_sparksql_sample.sql
└── src
├── mocks
│ ├── dataflow_pyspark_sample.yaml
│ └── some_db.source_table.yaml
├── sparksql_write.sql
└── test_sparksql_write.py
Typically, we choose to execute DDL instructions as a part of the workflow itself, as a substitute of working outdoors of the schedule, as a result of it simplifies the event course of. See under instance of hooking the desk creation SQL file into the primary workflow definition.
- job:
id: ddl
sort: Spark
spark:
script: $S3./ddl/dataflow_sparksql_sample.sql
parameters:
TARGET_DB: $TARGET_DB
Metadata
The metadata step offers context on the output desk itself in addition to the info contained inside. Attributes are set through Metacat, which is a Netflix inside metadata administration platform. Beneath is an instance of plugging that metadata step within the primary workflow definition
- job:
id: metadata
sort: Metadata
metacat:
tables:
- $CATALOG/$TARGET_DB/$TARGET_TABLE
proprietor: $username
tags:
- dataflow
- pattern
lifetime: 123
column_types:
date: pk
country_code: pk
rank: pk
Transformation
The transformation step (or steps) could be executed within the developer’s language of alternative. The instance under is utilizing SparkSQL.
.
├── backfill.sch.yaml
├── every day.sch.yaml
├── primary.sch.yaml
├── ddl
│ └── dataflow_sparksql_sample.sql
└── src
├── mocks
│ ├── dataflow_pyspark_sample.yaml
│ └── some_db.source_table.yaml
├── sparksql_write.sql
└── test_sparksql_write.py
Optionally, this step can use the Write-Audit-Publish pattern to make sure that information is appropriate earlier than it’s made out there to the remainder of the corporate. See instance under:
- template:
id: wap
sort: wap
tables:
- $CATALOG/$DATABASE/$TABLE
write_jobs:
- job:
id: write
sort: Spark
spark:
script: $S3./src/sparksql_write.sql
Audits
Audit steps could be outlined to confirm information high quality. If a “blocking” audit fails, the job will halt and the write step shouldn’t be dedicated, so invalid information won’t be uncovered to customers. This step is non-obligatory and configurable, see a partial instance of an audit from the primary workflow under.
data_auditor:
audits:
- operate: columns_should_not_have_nulls
blocking: true
params:
desk: $TARGET_TABLE
columns:
- title_id
…
Excessive-Water-Mark Timestamp
A profitable write will sometimes be adopted by a metadata name to set the legitimate time (or high-water mark) of a dataset. This permits different processes, consuming our desk, to be notified and begin their processing. See an instance excessive water mark job from the primary workflow definition.
- job:
id: hwm
sort: HWM
metacat:
desk: $CATALOG/$TARGET_DB/$TARGET_TABLE
hwm_datetime: $EXECUTION_DATE
hwm_timezone: $EXECUTION_TIMEZONE
Unit Exams
Unit take a look at artifacts are additionally generated as a part of the pattern workflow construction. They consist of information mocks, the precise take a look at code, and a easy execution harness relying on the workflow language. See the bolded file under.
.
├── backfill.sch.yaml
├── every day.sch.yaml
├── primary.sch.yaml
├── ddl
│ └── dataflow_sparksql_sample.sql
└── src
├── mocks
│ ├── dataflow_pyspark_sample.yaml
│ └── some_db.source_table.yaml
├── sparksql_write.sql
└── test_sparksql_write.py
These unit exams are meant to check one “unit” of information rework in isolation. They are often run throughout improvement to rapidly seize code typos and syntax points, or throughout automated testing/deployment part, to make it possible for code adjustments haven’t damaged any exams.
We would like unit exams to run rapidly in order that we are able to have steady suggestions and quick iterations in the course of the improvement cycle. Operating code in opposition to a manufacturing database could be sluggish, particularly with the overhead required for distributed information processing programs like Apache Spark. Mocks permit you to run exams regionally in opposition to a small pattern of “actual” information to validate your transformation code performance.
Languages
Over time, the extraction of information from Netflix’s supply programs has grown to embody a wider vary of end-users, similar to engineers, information scientists, analysts, entrepreneurs, and different stakeholders. Specializing in comfort, Dataflow permits for these differing personas to go about their work seamlessly. A lot of our information customers make use of SparkSQL, pyspark, and Scala. A small however rising contingency of information scientists and analytics engineers use R, backed by the Sparklyr interface or different information processing instruments, like Metaflow.
With an understanding that the info panorama and the applied sciences employed by end-users will not be homogenous, Dataflow creates a malleable path ahead. It solidifies completely different recipes or repeatable templates for information extraction. Inside this part, we’ll preview a couple of strategies, beginning with sparkSQL and python’s method of making information pipelines with dataflow. Then we’ll segue into the Scala and R use circumstances.
To start, after putting in Dataflow, a person can run the next command to know the best way to get began.
$ dataflow pattern workflow --help
Dataflow (0.6.16)Utilization: dataflow pattern workflow [OPTIONS] RECIPE [TARGET_PATH]
Create a pattern workflow based mostly on chosen RECIPE and land it within the
specified TARGET_PATH.
Presently supported workflow RECIPEs are: spark-sql, pyspark,
scala and sparklyr.
If TARGET_PATH:
- if not specified, present listing is assumed
- factors to a listing, it will likely be used because the goal location
Choices:
--source-path TEXT Supply path of the pattern workflows.
--workflow-shortname TEXT Workflow quick identify.
--workflow-id TEXT Workflow ID.
--skip-info Skip the information in regards to the workflow pattern.
--help Present this message and exit.
As soon as once more, let’s assume we have now a listing known as stranger-data during which the person creates workflow templates in all 4 languages that Dataflow affords. To higher illustrate the best way to generate the pattern workflows utilizing Dataflow, let’s have a look at the complete command one would use to create one among these workflows, e.g:
$ cd stranger-data
$ dataflow pattern workflow spark-sql ./sparksql-workflow
By repeating the above command for every sort of transformation language we are able to arrive on the following listing construction:
.
├── pyspark-workflow
│ ├── primary.sch.yaml
│ ├── every day.sch.yaml
│ ├── backfill.sch.yaml
│ ├── ddl
│ │ └── ...
│ ├── src
│ │ └── ...
│ └── tox.ini
├── scala-workflow
│ ├── construct.gradle
│ └── ...
├── sparklyR-workflow
│ └── ...
└── sparksql-workflow
└── ...
Earlier we talked in regards to the enterprise logic of those pattern workflows and we confirmed the Spark SQL model of that instance information transformation. Now let’s talk about completely different approaches to writing the info in different languages.
PySpark
This partial pySpark code under can have the identical performance because the SparkSQL instance above, but it surely makes use of Spark dataframes Python interface.
def primary(args, spark):source_table_df = spark.desk(f"some_db.source_table)
viewing_by_title_country = (
source_table_df.choose("title_id", "country_code",
"view_hours")
.filter(col("date") == date)
.filter("title_id IS NOT NULL AND view_hours > 0")
.groupBy("title_id", "country_code")
.agg(F.sum("view_hours").alias("view_hours"))
)
window = Window.partitionBy(
"country_code"
).orderBy(col("view_hours").desc())
ranked_viewing_by_title_country = viewing_by_title_country.withColumn(
"title_rank", rank().over(window)
)
ranked_viewing_by_title_country.filter(
col("title_rank") <= 100
).withColumn(
"date", lit(int(date))
).choose(
"title_id",
"country_code",
"title_rank",
"view_hours",
"date",
).repartition(1).write.byName().insertInto(
target_table, overwrite=True
)
Scala
Scala is one other Dataflow supported recipe that gives the identical enterprise logic in a pattern workflow out of the field.
package deal com.netflix.sparkobject ExampleApp
import spark.implicits._
def readSourceTable(sourceDb: String, dataDate: String): DataFrame =
spark
.desk(s"$someDb.source_table")
.filter($"playback_start_date" === dataDate)
def viewingByTitleCountry(sourceTableDF: DataFrame): DataFrame =
sourceTableDF
.choose($"title_id", $"country_code", $"view_hours")
.filter($"title_id".isNotNull)
.filter($"view_hours" > 0)
.groupBy($"title_id", $"country_code")
.agg(F.sum($"view_hours").as("view_hours"))
def addTitleRank(viewingDF: DataFrame): DataFrame =
viewingDF.withColumn(
"title_rank", F.rank().over(
Window.partitionBy($"country_code").orderBy($"view_hours".desc)
)
)
def writeViewing(viewingDF: DataFrame, targetTable: String, dataDate: String): Unit =
viewingDF
.choose($"title_id", $"country_code", $"title_rank", $"view_hours")
.filter($"title_rank" <= 100)
.repartition(1)
.withColumn("date", F.lit(dataDate.toInt))
.writeTo(targetTable)
.overwritePartitions()
def primary():
sourceTableDF = readSourceTable("some_db", "source_table", 20200101)
viewingDf = viewingByTitleCountry(sourceTableDF)
titleRankedDf = addTitleRank(viewingDF)
writeViewing(titleRankedDf)
R / sparklyR
As Netflix has a rising cohort of R customers, R is the most recent recipe out there in Dataflow.
suppressPackageStartupMessages(
library(sparklyr)
library(dplyr)
)...
primary <- operate(args, spark) >
ungroup()
primary(args = args, spark = spark)