Avro vs Protobuf Serialization Formats

http://guidoschmutz@wordpress.com@gschmutz
Big Data, Data Lake, Datenserialisierungsformate
Guido Schmutz

Guido
Working at Trivadis for more than 23 years
Consultant, Trainer, Platform Architect for Java,
Oracle, SOA and Big Data / Fast Data
Oracle Groundbreaker Ambassador & Oracle ACE
Director
@gschmutz guidoschmutz.wordpress.com
210th
edition

Agenda
• Introduction
• Avro vs. Protobuf
• Serialization in Big Data, Data Lake & Fast Data
• Protobuf and gRPC
• Summary
https://bit.ly/2zz0CV4
4

What is Serialization / Deserialization ?
Serialization is the process of turning structured in-memory objects into a byte stream for transmission
over a network or for writing to persistent storage
Deserialization is the reverse process from a byte stream back to a series of structured in-memory
objects
When selecting a data serialization format, the following characteristics should be evaluated:
• Schema support and Schema evolution
• Code generation
• Language support / Interoperability
• Transparent compression
• Splitability
• Support in Big Data / Fast Data Ecosystem
6

Where do we need Serialization / Deserialization ?
Service / Client
Logic
Event
Broker
Publish-
Subscribe
Data Lake
Service
{ }
API Logic
REST
Parallel
ProcessingStorage
Raw
serialize deserialize
serializedeserialize
serialize
deserialize
deserialize
serialize
Storage
Refined
Integration
Data Flow
serialize
Stream Analyticsdeserialize
serialize
ResultsStream Analytics
Streaming
Source
serialize
7

Sample Data Structured used in this presentation
Person (1.0)
• id : integer
• firstName : text
• lastName : text
• title :
enum(unknown,mr,mrs,ms)
• emailAddress : text
• phoneNumber : text
• faxNumber : text
• dateOfBirth : date
• addresses : array<Address>
Address (1.0)
• streetAndNr : text
• zipAndCity : text
{
"id":"1",
"firstName":"Peter",
"lastName":"Sample",
"title":"mr",
"emailAddress":"peter.sample@somecorp.com",
"phoneNumber":"+41 79 345 34 44",
"faxNumber":"+41 31 322 33 22",
"dateOfBirth":"1995-11-10",
"addresses":[
{
"id":"1",
"streetAndNr":"Somestreet 10",
"zipAndCity":"9332 Somecity"
}
]
}
https://github.com/gschmutz/various-demos/tree/master/avro-vs-protobuf
8

Google Protocol Buffers
• https://developers.google.com/protocol-buffers/
• Protocol buffers (protobuf) are Google's language-neutral, platform-neutral, extensible mechanism
for serializing structured data
• like XML, but smaller, faster, and simpler
• Schema is needed to generate code
and read/write data
• Support generated code in Java, Python,
Objective-C, C++, Go, Ruby, and C#
• Two different versions: proto2 and proto3
• Presentation based on proto3
• Latest version: 3.13.0
10

Apache Avro
• http://avro.apache.org/docs/current/
• Apache Avro™ is a compact, fast, binary data serialization system invented by the makers of Hadoop
• Avro relies on schemas. When data
is read, the schema used when writing
it is always present
• container file for storing persistent data
• Works both with code generation as well
as in a dynamic manner
• Latest version: 1.10.0
11

Overview
.avdl
file
Serialized
Data
Specific Record
Generic Record
Generator
.avsc
file
Java
C#
Python
Go
…
Java
C#
Python
Go
…
Serialized
Data
Message
Generator
.proto
file
Java
C#
Python
Go
…
12

Defining Schema - IDL
syntax = "proto3";
package com.trivadis.protobuf.person.v1;
import "address-v1.proto";
import "title-enum-v1.proto";
import "google/protobuf/timestamp.proto";
option java_outer_classname = "PersonWrapper";
message Person {
int32 id = 1;
string first_name = 2;
string last_name = 3;
com.Trivadis.protobuf.lov.Title title = 4;
string email_address = 5;
string phone_number = 6;
string fax_number = 7;
google.protobuf.Timestamp date_of_birth = 8;
repeated trivadis.protobuf.address.v1.Addresss
addresses = 9;
}
syntax = "proto3";
package com.trivadis.protobuf.lov;
enum Title {
UNKNOWN = 0;
MR = 1;
MRS = 2;
MS = 3;
}
syntax = "proto3";
package
com.trivadis.protobuf.address.v1;
message Addresss {
int32 id = 1;
string street_and_nr = 2;
string zip_and_city = 3;
}
person-v1.proto title-v1.proto
address-v1.proto
https://developers.google.com/protocol-buffers/docs/proto3
13

Defining Schema – JSON
Person-v1.avsc
{
"type" : "record",
"namespace" : "com.trivadis.avro.person.v1",
"name" : "Person",
"description" : "the representation of a person",
"fields" : [
{ "name": "id", "type": "int" },
{ "name": "firstName", "type": "string" },
{ "name": "lastName", "type": "string" },
{ "name" : "title", "type" : {
"type" : "enum", "name" : "TitleEnum",
"symbols" : ["Unknown", "Mr", "Mrs", "Ms"]
}
},
{ "name": "emailAddress", "type": ["null","string"] },
{ "name": "phoneNumber", "type": ["null","string"] },
{ "name": "faxNumber", "type": ["null","string"] },
{ "name": "dateOfBirth", "type": {
"type": "int", "logicalType": "date" } },
{ "name" : "addresses", ... }
]
}
https://avro.apache.org/docs/current/spec.html
14

Defining Schema - IDL
@namespace("com.trivadis.avro.person.v1")
protocol PersonIdl {
import idl "Address-v1.avdl";
enum TitleEnum {
Unknown, Mr, Ms, Mrs
}
record Person {
int id;
string firstName;
string lastName;
TitleEnum title;
union { null, string } emailAddress;
union { null, string } phoneNumber;
union { null, string } faxNumber;
date dateOfBirth;
array<com.trivadis.avro.address.v1.Address> addresses;
}
}
@namespace("com.trivadis.avro.address.v1")
protocol AddressIdl {
record Address {
int id;
string streetAndNr;
string zipAndCity;
}
}
Note: JSON Schema can be
generated from IDL Schema using
Avro Tools
address-v1.avdl
Person-v1.avdl
https://avro.apache.org/docs/current/idl.html
15

Defining Schema - Specification
• Multiple message types can be defined in
single proto file
• Field Numbers – each field in the message has
a unique number
• used to identify the fields in the message
binary format
• should not be changed once message type is in
use
• 1 – 15 uses single byte, 16 – 2047 uses two
bytes to encode
• Default values are type-specific
• Schema can either be represented as JSON or
by using the IDL
• Avro specifies two serialization encodings:
binary and JSON
• Encoding is done in order of fields defined in
record
• schema used to write data always needs to be
available when the data is read
• Schema can be serialized with the data or
• Schema is made available through registry
16

Defining Schema - Data Types
• Scalar Types
• double, float, int32, int64, uint32, uint64, sint32,
sint64, fixed32, fixed64, sfixed32, sfixed64
• bool
• string
• bytes
• Embedded Messages
• Enumerations
• Repeated
• Scalar Types
• null
• int, long, float, double
• boolean
• string
• bytes
• Records
• Map (string, Schema)
• Arrays (Schema)
• Enumerations
• Union
• Logical Types
17

Defining Schema - Style Guides
• Use CamelCase (with an initial capital) for
message names
• Use underscore_separated_names for
field names
enum type names
• Use CAPITALS_WITH_UNDERSCORES for
enum value names
• Use java-style comments for documenting
record names
• Use Camel Case for field names
enum type names
• Use CAPITALS_WITH_UNDERSCORES for
enum value names
• Use java-style comments (IDL) or doc property
(JSON) for documenting
18

IDE Support
Eclipse
• https://marketplace.eclipse.org/content/protobuf-dt
IntelliJ
• https://plugins.jetbrains.com/plugin/8277-protobuf-
support
Eclipse
• https://marketplace.eclipse.org/content/avroclipse
IntelliJ
• https://plugins.jetbrains.com/plugin/7971-apache-
avro-support
19

With Code Generation – Generate the code
• Run the protocol buffer compiler
• One compiler for all supported languages
• Produces classes for the given language
• Run the specific tool for the given language
• For Java
• For C++
• For C#
protoc -I=$SRC_DIR --java_out=$DST_DIR
$SRC_DIR/person-v1.proto
java -jar /path/to/avro-tools-1.8.2.jar
compile schema Person-v1.avsc .
avrogencpp -i cpx.json -o cpx.hh -n c
Microsoft.Hadoop.Avro.Tools codegen
/i:C:SDKsrcMicrosoft.Hadoop.Avro.Tool
sSampleJSONSampleJSONSchema.avsc /o:
20

With Code Generation – Using Maven
• Use protobuf-maven-plugin for
generating code at maven build
• Generates to target/generated-sources
• Scans all project dependencies for .proto files
• protoc has to be installed on machine
• Use avro-maven-plugin for generating
code at maven build
• Generates to target/generated-sources
21

Using Protobuf and Avro from Java
if you are using Maven, add the following dependency to your POM:
<dependency>
<groupId>org.apache.avro</groupId>
<artifactId>avro</artifactId>
<version>1.10.0</version>
</dependency>
<dependency>
<groupId>com.google.protobuf</groupId>
<artifactId>protobuf-java</artifactId>
</dependency>
22

With Code Generation – Create an instance
addresses.add(Address.newBuilder()
.setId(1)
.setStreetAndNr("Somestreet 10")
.setZipAndCity("9332 Somecity").build())
Person person = Person.newBuilder()
.setId(1)
.setFirstName("Peter")
.setLastName("Muster")
.setEmailAddress("peter.muster@somecorp.com")
.setPhoneNumber("+41 79 345 34 44")
.setFaxNumber("+41 31 322 33 22")
.setTitle(TitleEnum.Mr)
.setDateOfBirth(LocalDate.parse("1995-11-10"))
.setAddresses(addresses).build();
addresses.add(Addresss.newBuilder()
.setId(1)
.setStreetAndNr("Somestreet 10")
.setZipAndCity("9332 Somecity").build());
Instant time = Instant
.parse("1995-11-10T00:00:00.00Z");
Timestamp timestamp = Timestamp.newBuilder()
.setSeconds(time.getEpochSecond())
.setNanos(time.getNano()).build();
Person person = Person.newBuilder()
.setId(1)
.setFirstName("Peter")
.setLastName("Muster")
.setEmailAddress("peter.muster@somecorp.com")
.setPhoneNumber("+41 79 345 34 34")
.setFaxNumber("+41 31 322 33 22")
.setTitle(TitleEnumWrapper.Title.MR)
.setDateOfBirth(timestamp)
.addAllAddresses(addresses).build();
23

With Code Generation – Serializing
FileOutputStream fos = new FileOutputStream(BIN_FILE_NAME_V1));
ByteArrayOutputStream out = new ByteArrayOutputStream();
DatumWriter<Person> writer = new
SpecificDatumWriter<Person>(Person.getClassSchema());
writer.write(person, EncoderFactory.get().binaryEncoder(out, null));
encoder.flush();
out.close();
byte[] serializedBytes = out.toByteArray();
fos.write(serializedBytes);
FileOutputStream output = new
FileOutputStream(BIN_FILE_NAME_V2);
person.writeTo(output);
24

With Code Generation – Deserializing
DatumReader<Person> datumReader = new
SpecificDatumReader<Person>(Person.class);
byte[] bytes = Files.readAllBytes(new File(BIN_FILE_NAME_V1).toPath());
BinaryDecoder decoder = DecoderFactory.get().binaryDecoder(bytes, null);
Person person = datumReader.read(null, decoder);
System.out.println(person.getFirstName());
PersonWrapper.Person person =
PersonWrapper.Person.parseFrom(new
FileInputStream(BIN_FILE_NAME_V1));
System.out.println(person.getFirstName());
25

Encoding
• Field position (tag) are used as keys
• Variable length for int32 and int64
• + zig zag for sint32 and sint64
• data is serialized in field order of schema Variable
length, zig-zag for int and long, fixed length for float
and double
Variable length encoding: a method of serializing integers using one or more bytes
Zig Zag encoding: more efficient for negative numbers
26

Without code generation
final String schemaLoc = "src/main/avro/Person-v1.avsc";
final File schemaFile = new File(schemaLoc);
final Schema schema = new Schema.Parser().parse(schemaFile);
GenericRecord person1 = new GenericData.Record(schema);
person1.put("id", 1);
person1.put("firstName", "Peter");
person1.put("lastName", "Muster");
person1.put("title", "Mr");
person1.put("emailAddress", "peter.muster@somecorp.com");
person1.put("phoneNumber", "+41 79 345 34 44");
person1.put("faxNumber", "+41 31 322 33 22");
person1.put("dateOfBirth", new LocalDate("1995-11-10"));
27

Serializing to Object Container File
• file has schema and all
objects stored in the file
must be according to that
schema
• Objects are stored in
blocks that may be
compressed
final DatumWriter<Person> datumWriter = new
SpecificDatumWriter<>(Person.class);
final DataFileWriter<Person> dataFileWriter =
new DataFileWriter<>(datumWriter);
// use snappy compression
dataFileWriter.setCodec(CodecFactory.snappyCodec());
dataFileWriter.create(persons.get(0).getSchema(),
new File(CONTAINER_FILE_NAME_V1));
// specify block size
dataFileWriter.setSyncInterval(1000);
persons.forEach(person -> {
dataFileWriter.append(person);
});
28

Serializing to Object Container File
00000000: 4f62 6a01 0216 6176 726f 2e73 6368 656d 61f8 0d7b 2274 7970 6522 3a22 7265 636f 7264 Obj...avro.schema..{"type":"record
00000022: 222c 226e 616d 6522 3a22 5065 7273 6f6e 222c 226e 616d 6573 7061 6365 223a 2263 6f6d ","name":"Person","namespace":"com
00000044: 2e74 7269 7661 6469 732e 6176 726f 2e70 6572 736f 6e2e 7631 222c 2266 6965 6c64 7322 .trivadis.avro.person.v1","fields"
00000066: 3a5b 7b22 6e61 6d65 223a 2269 6422 2c22 7479 7065 223a 2269 6e74 222c 2264 6f63 223a :[{"name":"id","type":"int","doc":
00000088: 2269 6422 7d2c 7b22 6e61 6d65 223a 2266 6972 7374 4e61 6d65 222c 2274 7970 6522 3a22 "id"},{"name":"firstName","type":"
000000aa: 7374 7269 6e67 222c 2264 6f63 223a 2246 6972 7374 204e 616d 6522 7d2c 7b22 6e61 6d65 string","doc":"First Name"},{"name
000000cc: 223a 226c 6173 744e 616d 6522 2c22 7479 7065 223a 2273 7472 696e 6722 2c22 646f 6322 ":"lastName","type":"string","doc"
000000ee: 3a22 4c61 7374 204e 616d 6522 7d2c 7b22 6e61 6d65 223a 2274 6974 6c65 222c 2274 7970 :"Last Name"},{"name":"title","typ
00000110: 6522 3a7b 2274 7970 6522 3a22 656e 756d 222c 226e 616d 6522 3a22 5469 746c 6545 6e75 e":{"type":"enum","name":"TitleEnu
00000132: 6d22 2c22 646f 6322 3a22 5661 6c69 6420 7469 746c 6573 222c 2273 796d 626f 6c73 223a m","doc":"Valid titles","symbols":
00000154: 5b22 556e 6b6e 6f77 6e22 2c22 4d72 222c 224d 7273 222c 224d 7322 5d7d 2c22 646f 6322 ["Unknown","Mr","Mrs","Ms"]},"doc"
00000176: 3a22 7468 6520 7469 746c 6520 7573 6564 227d 2c7b 226e 616d 6522 3a22 656d 6169 6c41 :"the title used"},{"name":"emailA
00000198: 6464 7265 7373 222c 2274 7970 6522 3a5b 226e 756c 6c22 2c22 7374 7269 6e67 225d 2c22 ddress","type":["null","string"],"
000001ba: 646f 6322 3a22 227d 2c7b 226e 616d 6522 3a22 7068 6f6e 654e 756d 6265 7222 2c22 7479 doc":""},{"name":"phoneNumber","ty
000001dc: 7065 223a 5b22 6e75 6c6c 222c 2273 7472 696e 6722 5d2c 2264 6f63 223a 2222 7d2c 7b22 pe":["null","string"],"doc":""},{"
000001fe: 6e61 6d65 223a 2266 6178 4e75 6d62 6572 222c 2274 7970 6522 3a5b 226e 756c 6c22 2c22 name":"faxNumber","type":["null","
00000220: 7374 7269 6e67 225d 2c22 646f 6322 3a22 227d 2c7b 226e 616d 6522 3a22 6461 7465 4f66 string"],"doc":""},{"name":"dateOf
00000242: 4269 7274 6822 2c22 7479 7065 223a 7b22 7479 7065 223a 2269 6e74 222c 226c 6f67 6963 Birth","type":{"type":"int","logic
00000264: 616c 5479 7065 223a 2264 6174 6522 7d2c 2264 6f63 223a 2244 6174 6520 6f66 2042 6972 alType":"date"},"doc":"Date of Bir
00000286: 7468 227d 2c7b 226e 616d 6522 3a22 6164 6472 6573 7365 7322 2c22 7479 7065 223a 5b22 th"},{"name":"addresses","type":["
000002a8: 6e75 6c6c 222c 7b22 7479 7065 223a 2261 7272 6179 222c 2269 7465 6d73 223a 7b22 7479 null",{"type":"array","items":{"ty
000002ca: 7065 223a 2272 6563 6f72 6422 2c22 6e61 6d65 223a 2241 6464 7265 7373 222c 2266 6965 pe":"record","name":"Address","fie
000002ec: 6c64 7322 3a5b 7b22 6e61 6d65 223a 2269 6422 2c22 7479 7065 223a 2269 6e74 227d 2c7b lds":[{"name":"id","type":"int"},{
0000030e: 226e 616d 6522 3a22 7374 7265 6574 416e 644e 7222 2c22 7479 7065 223a 2273 7472 696e "name":"streetAndNr","type":"strin
00000330: 6722 7d2c 7b22 6e61 6d65 223a 227a 6970 416e 6443 6974 7922 2c22 7479 7065 223a 2273 g"},{"name":"zipAndCity","type":"s
00000352: 7472 696e 6722 7d5d 7d7d 5d7d 5d2c 2264 6573 6372 6970 7469 6f6e 223a 2274 6865 2072 tring"}]}}]}],"description":"the r
00000374: 6570 7265 7365 6e74 6174 696f 6e20 6f66 2061 2070 6572 736f 6e22 7d00 111d 965a be54 epresentation of a person"}....Z.T
00000396: 3682 1242 1863 02c2 982c 12f2 0f02 0a50 6574 6572 0c53 616d 706c 6502 0232 7065 7465 6..B.c...,.....Peter.Sample..2pete
000003b8: 722e 7361 6d70 6c65 4073 6f6d 6563 6f72 702e 636f 6d02 202b 3431 2037 3920 3334 3520 r.sample@somecorp.com. +41 79 345
000003da: 3334 2034 3402 202b 3431 2033 3120 3332 3220 3333 2032 32c8 9301 0202 021a 536f 6d65 34 44. +41 31 322 33 22.......Some
Avro Container File contains a header with the
Avro Schema used when writing the data
Synchronization markers are used between
data blocks to permit efficient splitting of files
29

Schema Evolution
Person (1.0)
• id : integer
• lastName : text
• title : enum(unknown,mr,mrs,ms)
• dateOfBirth : date
• addresses : array<Address>
Address (1.0)
Person (1.1)
• id : integer
• middleName : text
• lastName : text
• title : enum(unknown,mr,mrs,ms)
• addresses : array<Addresss>
Address (1.0)
V1.0 to V1.1
• Adding
middleName
• Remove
faxNumber
30

Schema Evolution
message Person {
int32 id = 1;
com.Trivadis.protobuf.lov.Title title = 4;
string fax_number = 7;
google.protobuf.Timestamp
date_of_birth = 8;
addresses = 9;
}
person-v1.proto
person-v1.1.proto
message Person {
int32 id = 1;
string middle_name = 10;
com.trivadis.protobuf.lov.Title title = 4;
// string fax_number = 7;
google.protobuf.Timestamp
birth_date = 8;
addresses = 9;
}
31

Schema Evolution
1 1
2 Peter
3 Sample
4 MR
5 peter.sample@somecorp.com
6 +41 79 345 34 44
7 +41 31 322 33 22
8 1995-11-10
9 1 1
9 2 Somestreet 10
9 3 9332 Somecity
1 1
2 Peter
3 Sample
4 MR
5 peter.sample@somecorp.com
6 +41 79 345 34 44
8 1995-11-10
9 1 1
9 2 Somestreet 10
9 3 9332 Somecity
10 Paul
unknown fields
7 +41 31 322 33 22
V1.0
V1.1
V1.0 to V1.1
32

Person-v1.avsc
Schema Evolution
{
"type" : "record",
"namespace" : "com.trivadis.avro.person.v1",
"name" : "Person",
"description" : "the representation of a person",
"fields" : [
{ "name" : "title", "type" : {
"type" : "enum", "name" : "TitleEnum",
"symbols" : ["Unknown", "Mr", "Mrs", "Ms"]
}
},
{ "name": "faxNumber", "type": ["null","string"] },
{ "name": "dateOfBirth", "type": {
"type": "int", "logicalType": "date" } },
{ "name" : "addresses", ... }
]
}
{
"type" : "record",
...
"fields" : [
{ "name": "middleName",
"type": ["null","string"], "default":null },
{ "name" : "title", ... },
{ "name" : "addresses", ... },
]
}
Person-v1.1.avsc
33

Schema Evolution
id 1
firstName Peter
lastName Sample
title MR
emailAddress peter.sample@somecorp.com
phoneNumber +41 79 345 34 44
faxNumber +41 31 322 33 22
dateOfBirth 1995-11-10
addresses.id 1
addresses.streetAndNr Somestreet 10
addresses.zipAndCity 9332 Somecity
V1.0 to V1.1
V1.0 V1.1
id 1
firstName Peter
middleName Paul
lastName Sample
title MR
emailAddress peter.sample@somecorp.com
phoneNumber +41 79 345 34 44
dateOfBirth 1995-11-10
addresses.id 1
addresses.streetAndNr Somestreet 10
addresses.zipAndCity 9332 Somecity
34

Avro & Protobuf with Kafka
Source
Connector
Kafka Broker
Sink
Connector
Stream
Processing
Schema
Registry
Kafka Kafka
36

Avro and Kafka – Schema Registry
<plugin>
<groupId>io.confluent</groupId>
<artifactId>kafka-schema-registry-maven-plugin</artifactId>
<configuration>
<schemaRegistryUrls>
<param>http://172.16.10.10:8081</param>
</schemaRegistryUrls>
<subjects>
<person-v1-value>src/main/avro/Person-v1.avsc</person-v1-value>
</subjects>
</configuration>
<goals>
<goal>register</goal>
</goals>
</plugin>
mnv schema-registry:register
curl -X "GET" "http://172.16.10.10:8081/subjects"
37

Avro and Kafka – Producing Avro to Kafka
@Configuration
public class KafkaConfig {
private String bootstrapServers;
private String schemaRegistryURL;
@Bean
public Map<String, Object> producerConfigs() {
Map<String, Object> props = new HashMap<>();
props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, bootstrapServers);
props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class);
props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, KafkaAvroSerializer.class);
props.put(KafkaAvroSerializerConfig.SCHEMA_REGISTRY_URL_CONFIG, schemaRegistryURL);
return props;
}
@Bean
public ProducerFactory<String, Person> producerFactory() { .. }
@Bean
public KafkaTemplate<String, Person> kafkaTemplate() {
return new KafkaTemplate<>(producerFactory());
}
@Component
public class PersonEventProducer {
@Autowired
private KafkaTemplate<String, Person> kafkaTemplate;
@Value("${kafka.topic.person}")
String kafkaTopic;
public void produce(Person person) {
kafkaTemplate.send(kafkaTopic, person.getId().toString(), person);
}
}
38

Avro and Big Data
• Avro is widely supported by Big Data Frameworks: Hadoop MapReduce, Pig, Hive, Sqoop, Apache
Spark, …
• Spark Avro DataSource for Apache Spark supports using Avro as a source for DataFrames:
https://spark.apache.org/docs/latest/sql-data-sources-avro.html
import com.databricks.spark.avro._
val personDF = spark.read.avro("person-v1.avro")
personDF.createOrReplaceTempView("person")
val subPersonDF =
spark.sql("select * from person where firstName like 'G%'")
libraryDependencies += ”org.apache.spark" %% "spark-avro" % ”2.12:3.0.1"
39

There is more! Column-oriented: Apache Parquet and ORC
A logical table can be translated using either
• Row-based layout (Avro, Protobuf, JSON, …)
• Column-oriented layout (Parquet, ORC, …)
Apache Parquet
• collaboration between Twitter and
Cloudera
• Support in Hadoop, Hive, Spark, Apache
NiFi, StreamSets, Apache Pig, …
Apache ORC
• was created by Facebook and
Hortonworks
• Support in Hadoop, Hive, Spark, Apache
NiFi, Apache Pig, Presto, …
A B C
A1 B1 C1
A2 B2 C2
A2 B2 C2
A1 B1 C1 A2 B2 C2 A3 B3 C3
A1 A2 A3 B1 B2 B3 C1 C2 C3
40

Parquet and Big Data
• Avro is widely supported by Big Data Frameworks: Hadoop MapReduce, Pig, Hive, Sqoop, Apache
Spark, …
• Spark Avro DataSource for Apache Spark supports using Avro as a source for DataFrames:
https://github.com/databricks/spark-avro
import com.databricks.spark.avro._
val personDF = spark.read.avro("person-v1.avro")
personDF.createOrReplaceTempView("Person")
val subPersonDF =
spark.sql("select * from Person where firstName like 'G%'")
libraryDependencies += ”org.apache.spark" %% "spark-avro" % ”2.12:3.0.1"
41

Delta Lake - http://delta.io
• Delta Lake is an open source storage layer that brings reliability to data lakes
• First part of Databricks Platform, now open-sourced
• Delta Lake provides
• Fully compatible with Apache Spark
• ACID transactions
• Update and Delete on Big Data Storage
• Schema enforcement
• Time Travel (Data versioning)
• Scalable metadata handling
• Open Format (Parquet)
• Unified streaming and batch data processing
• Schema Evolution
• Audit History
• Integration with Presto/Athena/Hive/Amazon Redshift/Snowflake for read
42

Delta Lake - http://delta.io
43

Other Delta Lake Storage Layers
Apache Hudi
• https://hudi.apache.org/
• Ingests & manages storage of large analytical datasets over DFS
Apache Iceberg
• https://iceberg.apache.org
• Open table format for huge analytic datasets
• Adds tables to Presto and Spark that use a high-performance format
44
https://medium.com/@domisj/comparison-of-big-data-storage-layers-delta-vs-apache-hudi-vs-apache-iceberg-part-1-200599645a02

Protobuf and gRPC
• https://grpc.io/
• Google's high performance, open-source
universal RPC framework
• layering on top of HTTP/2 and using protocol
buffers to define messages
• Support for Java, C#, C++, Python, Go, Ruby,
Node.js, Objective-C, …
Source: https://thenewstack.io
46

Serialization / Deserialization
Service / Client
Logic
Event
Broker
Publish-
Subscribe
Data Lake
Service
{ }
API Logic
REST
Parallel
ProcessingStorage
Raw
serialize deserialize
serialize
deserialize
deserialize
serialize
Storage
Refined
Integration
Data Flow
serialize
Stream Analyticsdeserialize
serialize
ResultsStream Analytics
Streaming
Source
serialize
48

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We're looking forward to meeting you.
Link to the Expo area:
https://www.vinivia-event-
manager.io/e/DOAG/portal/expo/29731

Avro vs Protobuf Serialization Formats

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