Thursday, May 30, 2024

A side-by-side comparability of Apache Spark and Apache Flink for widespread streaming use instances


Apache Flink and Apache Spark are each open-source, distributed information processing frameworks used broadly for large information processing and analytics. Spark is understood for its ease of use, high-level APIs, and the flexibility to course of giant quantities of information. Flink shines in its potential to deal with processing of information streams in real-time and low-latency stateful computations. Each help quite a lot of programming languages, scalable options for dealing with giant quantities of information, and a variety of connectors. Traditionally, Spark began out as a batch-first framework and Flink started as a streaming-first framework.

On this put up, we share a comparative examine of streaming patterns which can be generally used to construct stream processing purposes, how they are often solved utilizing Spark (primarily Spark Structured Streaming) and Flink, and the minor variations of their strategy. Examples cowl code snippets in Python and SQL for each frameworks throughout three main themes: information preparation, information processing, and information enrichment. In case you are a Spark consumer trying to remedy your stream processing use instances utilizing Flink, this put up is for you. We don’t intend to cowl the selection of know-how between Spark and Flink as a result of it’s essential to guage each frameworks on your particular workload and the way the selection suits in your structure; moderately, this put up highlights key variations to be used instances that each these applied sciences are generally thought-about for.

Apache Flink gives layered APIs that supply completely different ranges of expressiveness and management and are designed to focus on various kinds of use instances. The three layers of API are Course of Capabilities (also referred to as the Stateful Stream Processing API), DataStream, and Desk and SQL. The Stateful Stream Processing API requires writing verbose code however gives probably the most management over time and state, that are core ideas in stateful stream processing. The DataStream API helps Java, Scala, and Python and gives primitives for a lot of widespread stream processing operations, in addition to a stability between code verbosity or expressiveness and management. The Desk and SQL APIs are relational APIs that supply help for Java, Scala, Python, and SQL. They provide the best abstraction and intuitive, SQL-like declarative management over information streams. Flink additionally permits seamless transition and switching throughout these APIs. To be taught extra about Flink’s layered APIs, seek advice from layered APIs.

Apache Spark Structured Streaming gives the Dataset and DataFrames APIs, which give high-level declarative streaming APIs to symbolize static, bounded information in addition to streaming, unbounded information. Operations are supported in Scala, Java, Python, and R. Spark has a wealthy operate set and syntax with easy constructs for choice, aggregation, windowing, joins, and extra. You may as well use the Streaming Desk API to learn tables as streaming DataFrames as an extension to the DataFrames API. Though it’s arduous to attract direct parallels between Flink and Spark throughout all stream processing constructs, at a really excessive degree, let’s imagine Spark Structured Streaming APIs are equal to Flink’s Desk and SQL APIs. Spark Structured Streaming, nonetheless, doesn’t but (on the time of this writing) provide an equal to the lower-level APIs in Flink that supply granular management of time and state.

Each Flink and Spark Structured Streaming (referenced as Spark henceforth) are evolving initiatives. The next desk offers a easy comparability of Flink and Spark capabilities for widespread streaming primitives (as of this writing).

. Flink Spark
Row-based processing Sure Sure
Consumer-defined features Sure Sure
High quality-grained entry to state Sure, by way of DataStream and low-level APIs No
Management when state eviction happens Sure, by way of DataStream and low-level APIs No
Versatile information buildings for state storage and querying Sure, by way of DataStream and low-level APIs No
Timers for processing and stateful operations Sure, by way of low degree APIs No

Within the following sections, we cowl the best widespread components in order that we will showcase how Spark customers can relate to Flink and vice versa. To be taught extra about Flink’s low-level APIs, seek advice from Course of Operate. For the sake of simplicity, we cowl the 4 use instances on this put up utilizing the Flink Desk API. We use a mixture of Python and SQL for an apples-to-apples comparability with Spark.

Information preparation

On this part, we examine information preparation strategies for Spark and Flink.

Studying information

We first take a look at the best methods to learn information from a knowledge stream. The next sections assume the next schema for messages:

image: string,
worth: int,
timestamp: timestamp,
company_info:
{
    identify: string,
    employees_count: int
}

Studying information from a supply in Spark Structured Streaming

In Spark Structured Streaming, we use a streaming DataFrame in Python that instantly reads the information in JSON format:

spark = ...  # spark session

# specify schema
stock_ticker_schema = ...

# Create a streaming DataFrame
df = spark.readStream 
    .format("kafka") 
    .choice("kafka.bootstrap.servers", "mybroker1:port") 
    .choice("matter", "stock_ticker") 
    .load()
    .choose(from_json(col("worth"), stock_ticker_schema).alias("ticker_data")) 
    .choose(col("ticker_data.*"))

Notice that we have now to provide a schema object that captures our inventory ticker schema (stock_ticker_schema). Evaluate this to the strategy for Flink within the subsequent part.

Studying information from a supply utilizing Flink Desk API

For Flink, we use the SQL DDL assertion CREATE TABLE. You may specify the schema of the stream similar to you’ll any SQL desk. The WITH clause permits us to specify the connector to the information stream (Kafka on this case), the related properties for the connector, and information format specs. See the next code:

# Create desk utilizing DDL

CREATE TABLE stock_ticker (
  image string,
  worth INT,
  timestamp TIMESTAMP(3),
  company_info STRING,
  WATERMARK FOR timestamp AS timestamp - INTERVAL '3' MINUTE
) WITH (
 'connector' = 'kafka',
 'matter' = 'stock_ticker',
 'properties.bootstrap.servers' = 'mybroker1:port',
 'properties.group.id' = 'testGroup',
 'format' = 'json',
 'json.fail-on-missing-field' = 'false',
 'json.ignore-parse-errors' = 'true'
)

JSON flattening

JSON flattening is the method of changing a nested or hierarchical JSON object right into a flat, single-level construction. This converts a number of ranges of nesting into an object the place all of the keys and values are on the identical degree. Keys are mixed utilizing a delimiter reminiscent of a interval (.) or underscore (_) to indicate the unique hierarchy. JSON flattening is helpful when you should work with a extra simplified format. In each Spark and Flink, nested JSONs could be sophisticated to work with and might have further processing or user-defined features to control. Flattened JSONs can simplify processing and enhance efficiency as a consequence of decreased computational overhead, particularly with operations like complicated joins, aggregations, and windowing. As well as, flattened JSONs might help in simpler debugging and troubleshooting information processing pipelines as a result of there are fewer ranges of nesting to navigate.

JSON flattening in Spark Structured Streaming

JSON flattening in Spark Structured Streaming requires you to make use of the choose technique and specify the schema that you simply want flattened. JSON flattening in Spark Structured Streaming includes specifying the nested area identify that you simply’d like surfaced to the top-level record of fields. Within the following instance, company_info is a nested area and inside company_info, there’s a area known as company_name. With the next question, we’re flattening company_info.identify to company_name:

stock_ticker_df = ...  # Streaming DataFrame w/ schema proven above

stock_ticker_df.choose("image", "timestamp", "worth", "company_info.identify" as "company_name")

JSON flattening in Flink

In Flink SQL, you should utilize the JSON_VALUE operate. Notice that you should utilize this operate solely in Flink variations equal to or higher than 1.14. See the next code:

SELECT
   image,
   timestamp,
   worth,
   JSON_VALUE(company_info, 'lax $.identify' DEFAULT NULL ON EMPTY) AS company_name
FROM
   stock_ticker

The time period lax within the previous question has to do with JSON path expression dealing with in Flink SQL. For extra data, seek advice from System (Constructed-in) Capabilities.

Information processing

Now that you’ve got learn the information, we will take a look at just a few widespread information processing patterns.

Deduplication

Information deduplication in stream processing is essential for sustaining information high quality and guaranteeing consistency. It enhances effectivity by decreasing the pressure on the processing from duplicate information and helps with value financial savings on storage and processing.

Spark Streaming deduplication question

The next code snippet is said to a Spark Streaming DataFrame named stock_ticker. The code performs an operation to drop duplicate rows based mostly on the image column. The dropDuplicates technique is used to get rid of duplicate rows in a DataFrame based mostly on a number of columns.

stock_ticker = ...  # Streaming DataFrame w/ schema proven above

stock_ticker.dropDuplicates("image")

Flink deduplication question

The next code reveals the Flink SQL equal to deduplicate information based mostly on the image column. The question retrieves the primary row for every distinct worth within the image column from the stock_ticker stream, based mostly on the ascending order of proctime:

SELECT image, timestamp, worth
FROM (
  SELECT *,
    ROW_NUMBER() OVER (PARTITION BY image ORDER BY proctime ASC) AS row_num
  FROM stock_ticker)
WHERE row_num = 1

Windowing

Windowing in streaming information is a basic assemble to course of information inside specs. Home windows generally have time bounds, variety of data, or different standards. These time bounds bucketize steady unbounded information streams into manageable chunks known as home windows for processing. Home windows assist in analyzing information and gaining insights in actual time whereas sustaining processing effectivity. Analyses or operations are carried out on continually updating streaming information inside a window.

There are two widespread time-based home windows used each in Spark Streaming and Flink that we’ll element on this put up: tumbling and sliding home windows. A tumbling window is a time-based window that may be a fastened dimension and doesn’t have any overlapping intervals. A sliding window is a time-based window that may be a fastened dimension and strikes ahead in fastened intervals that may be overlapping.

Spark Streaming tumbling window question

The next is a Spark Streaming tumbling window question with a window dimension of 10 minutes:

stock_ticker = ...  # Streaming DataFrame w/ schema proven above

# Get max inventory worth in tumbling window
# of dimension 10 minutes
visitsByWindowAndUser = visits
   .withWatermark("timestamp", "3 minutes")
   .groupBy(
      window(stock_ticker.timestamp, "10 minutes"),
      stock_ticker.image)
   .max(stock_ticker.worth)

Flink Streaming tumbling window question

The next is an equal tumbling window question in Flink with a window dimension of 10 minutes:

SELECT image, MAX(worth)
  FROM TABLE(
    TUMBLE(TABLE stock_ticker, DESCRIPTOR(timestamp), INTERVAL '10' MINUTES))
  GROUP BY ticker;

Spark Streaming sliding window question

The next is a Spark Streaming sliding window question with a window dimension of 10 minutes and slide interval of 5 minutes:

stock_ticker = ...  # Streaming DataFrame w/ schema proven above

# Get max inventory worth in sliding window
# of dimension 10 minutes and slide interval of dimension
# 5 minutes

visitsByWindowAndUser = visits
   .withWatermark("timestamp", "3 minutes")
   .groupBy(
      window(stock_ticker.timestamp, "10 minutes", "5 minutes"),
      stock_ticker.image)
   .max(stock_ticker.worth)

Flink Streaming sliding window question

The next is a Flink sliding window question with a window dimension of 10 minutes and slide interval of 5 minutes:

SELECT image, MAX(worth)
  FROM TABLE(
    HOP(TABLE stock_ticker, DESCRIPTOR(timestamp), INTERVAL '5' MINUTES, INTERVAL '10' MINUTES))
  GROUP BY ticker;

Dealing with late information

Each Spark Structured Streaming and Flink help occasion time processing, the place a area inside the payload can be utilized for outlining time home windows as distinct from the wall clock time of the machines doing the processing. Each Flink and Spark use watermarking for this objective.

Watermarking is utilized in stream processing engines to deal with delays. A watermark is sort of a timer that units how lengthy the system can await late occasions. If an occasion arrives and is inside the set time (watermark), the system will use it to replace a request. If it’s later than the watermark, the system will ignore it.

Within the previous windowing queries, you specify the lateness threshold in Spark utilizing the next code:

.withWatermark("timestamp", "3 minutes")

Which means any data which can be 3 minutes late as tracked by the occasion time clock will probably be discarded.

In distinction, with the Flink Desk API, you may specify a similar lateness threshold instantly within the DDL:

WATERMARK FOR timestamp AS timestamp - INTERVAL '3' MINUTE

Notice that Flink offers further constructs for specifying lateness throughout its varied APIs.

Information enrichment

On this part, we examine information enrichment strategies with Spark and Flink.

Calling an exterior API

Calling exterior APIs from user-defined features (UDFs) is analogous in Spark and Flink. Notice that your UDF will probably be known as for each file processed, which can lead to the API getting known as at a really excessive request fee. As well as, in manufacturing eventualities, your UDF code usually will get run in parallel throughout a number of nodes, additional amplifying the request fee.

For the next code snippets, let’s assume that the exterior API name entails calling the operate:

response = my_external_api(request)

Exterior API name in Spark UDF

The next code makes use of Spark:

class Predict(ScalarFunction):
def open(self, function_context):

with open("sources.zip/sources/mannequin.pkl", "rb") as f:
self.mannequin = pickle.load(f)

def eval(self, x):
return self.mannequin.predict(x)

Exterior API name in Flink UDF

For Flink, assume we outline the UDF callExternalAPIUDF, which takes as enter the ticker image image and returns enriched details about the image by way of a REST endpoint. We will then register and name the UDF as follows:

callExternalAPIUDF = udf(callExternalAPIUDF(), result_type=DataTypes.STRING())

SELECT
    image, 
    callExternalAPIUDF(image) as enriched_symbol
FROM stock_ticker;

Flink UDFs present an initialization technique that will get run one time (versus one time per file processed).

Notice that you must use UDFs judiciously as an improperly applied UDF may cause your job to decelerate, trigger backpressure, and finally stall your stream processing software. It’s advisable to make use of UDFs asynchronously to keep up excessive throughput, particularly for I/O-bound use instances or when coping with exterior sources like databases or REST APIs. To be taught extra about how you should utilize asynchronous I/O with Apache Flink, seek advice from Enrich your information stream asynchronously utilizing Amazon Kinesis Information Analytics for Apache Flink.

Conclusion

Apache Flink and Apache Spark are each quickly evolving initiatives and supply a quick and environment friendly option to course of large information. This put up centered on the highest use instances we generally encountered when clients wished to see parallels between the 2 applied sciences for constructing real-time stream processing purposes. We’ve included samples that had been most regularly requested on the time of this writing. Tell us if you happen to’d like extra examples within the feedback part.


Concerning the writer

Deepthi Mohan is a Principal Product Supervisor on the Amazon Kinesis Information Analytics group.

Karthi Thyagarajan was a Principal Options Architect on the Amazon Kinesis group.

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