In real estate markets, it can make a difference to have real-time data instead of the good-old batch processing. How many real estate agents are losing time because they cannot find their listing? How many home-seekers lost opportunities, hearing things like “sorry, it’s already sold”? In this talk I will be presenting how we implemented an event-driven architecture based on Apache Kafka, how it is enabling us to deliver fresh content to all LifullConnect sites across 3 continents, and build meaningful products to real estate professionals, and home-seekers.

1. Real-time
real estate listings
with Apache Kafka
Ferran Galí i Reniu

2. What is LIFULL Connect?
The real-time listings system

3. What is a Real Estate Listing?

4. What is a Real Estate Listing?

5. Now...

6. What is LIFULL Connect?

7. WE
HELP PEOPLE
FIND A HOME
LIFULL Connect mission

8. What is LIFULL Connect?

9. LIFULL Connect

10. LIFULL Connect
MILLIONS
OF
PEOPLE
TO HELP

11. What is LIFULL Connect?
The real-time listings system

12. Connections
To connect
home-seekers
with listings…

13. …we should
connect them
with their owners
Connections

14. Challenge #1: Listing Publishing

15. Challenge #1: Listing Publishing

16. Challenge #1: Listing Publishing

17. We created a unified back-office, so
professionals can publish in a single
place
Challenge #1: Listing Publishing

18. Unified Back-office

19. Unified Back-office

20. Unified Back-office

21. Challenge #2: Freshness
Yeah! But…
10 sites
5 teams
8 legacy batch systems
4h-24h listing publication delay

22. Real Estate Professionals don’t care.
They want listings published, now.
(and we want to offer them the best experience)
Challenge #2: Freshness

23. Welcome Real-time Listings System
(Internally we call it ReTiS)
Challenge #2: Freshness

24. Goals
Real-time data propagation
Strangle legacy systems
Reduce complexity

25. Real-time Listings System

26. Real-time Listings System

27. Real-time Listings System

28. Real-time Listings System

29. Real-time Listings System

30. How we did it?

31. What is LIFULL Connect?
The real-time listings system
How we propagate data?
How we process listings?
How we serve listings?

32. How we propagate data?

33. How we propagate data?

34. How we propagate data?
Domain Events
+
Event Log

35. How we propagate data?
Domain events
A domain event is something that happened in a specific domain
that we want others to be aware of.

36. How we propagate data?
Event Log
It’s an append-only sequence of events.
It differs from a queue, because items are not consumed

37. How we propagate data?

38. How we propagate data?
ListingHasBeenPublished

39. How we propagate data?
ListingHasBeenPublished
ListingHasBeenModified

40. How we propagate data?
ListingHasBeenPublished
ListingHasBeenModified
ListingHasBeenHidden
ListingHasBeenModified
ListingHasBeenModified
ListingHasBeenPublished

41. How we propagate data?
ListingHasBeenPublished
ListingHasBeenModified
ListingHasBeenHidden
ListingHasBeenModified
ListingHasBeenModified
ListingHasBeenPublished

42. How we implemented it?
Apache Kafka
+
Apache Avro

43. Apache Avro
● Efficient engine to serialize data
● Libraries in multiple languages
● Schema is used to write and read data

44. Apache Avro
MyData
id:
c4d729f3-3d20-4b72-8ffe-c6dd828b9a2c
price: 233243.2
Datum

45. {
"type": "record",
"name": "MyData",
"namespace": "com.lifullconnect"
,
"doc": "This is the data"
,
"fields": [
{
"name": "id",
"type": "string"
},
{
"name": "price",
"type": "double"
"default": 0.0
}
]
}
Apache Avro
Schema
MyData
id:
c4d729f3-3d20-4b72-8ffe-c6dd828b9a2c
price: 233243.2
Datum

46. {
"type": "record",
"name": "MyData",
"namespace": "com.lifullconnect"
,
"doc": "This is the data"
,
"fields": [
{
"name": "id",
"type": "string"
},
{
"name": "price",
"type": "double"
"default": 0.0
}
]
}
Apache Avro
c4d729f3-3d20-4b72-8ffe-c6dd
828b9a2c�����234243.2�
Schema
Serialized
MyData
id:
c4d729f3-3d20-4b72-8ffe-c6dd828b9a2c
price: 233243.2
Datum

47. {
"type": "record",
"name": "MyData",
"namespace": "com.lifullconnect"
,
"doc": "This is the data"
,
"fields": [
{
"name": "id",
"type": "string"
},
{
"name": "price",
"type": "double"
"default": 0.0
}
]
}
Apache Avro
c4d729f3-3d20-4b72-8ffe-c6dd
828b9a2c�����234243.2�
Schema
Serialized
MyData
id:
c4d729f3-3d20-4b72-8ffe-c6dd828b9a2c
price: 233243.2
Datum

48. How we implemented it?
ListingHasBeenPublished
event_id: UUID //a unique ID
produced_by: String //owner of data
occurred_on: Date //when it happened
id: String //which ID in the domain refers
listing: …
…

49. How we implemented it?
ListingHasBeenPublished
event_id: UUID //a unique ID
produced_by: String //owner of data
occurred_on: Date //when it happened
id: String //which ID in the domain refers
listing: …
…
ListingHasBeenModified
event_id: UUID //a unique ID
produced_by: String //owner of data
occurred_on: Date //when it happened
id: String //which ID in the domain refers
modifications: …
…

50. How we implemented it?
ListingHasBeenPublished
event_id: UUID //a unique ID
produced_by: String //owner of data
occurred_on: Date //when it happened
id: String //which ID in the domain refers
listing: …
…
ListingHasBeenModified
event_id: UUID //a unique ID
produced_by: String //owner of data
occurred_on: Date //when it happened
id: String //which ID in the domain refers
modifications: …
…
ListingHasBeenBoosted
event_id: UUID //a unique ID
produced_by: String //owner of data
occurred_on: Date //when it happened
id: String //which ID in the domain refers
boosting: …
…

51. Apache Kafka
● Implements a log
● Distributed in a cluster of broker nodes
● Fault tolerant through partitions & replicas
● Enables multiple consumers of data
● Enables real-time consumption of data

52. Apache Kafka
Topic 1 Topic 2

53. Apache Kafka
Topic 1
Partition 2
Partition 3
Topic 2
Partition 2
Partition 1
Partition 1

54. Apache Kafka
Topic 1
Partition 1 Partition 2
Partition 3
Topic 2
Partition 1 Partition 2

55. Apache Kafka
Topic 1
Partition 1 Partition 2
Partition 3
Topic 2
Partition 1 Partition 2
Broker 1 Broker 2
Broker 3

56. Apache Kafka
Topic 1
Partition 1 Partition 2
Partition 3
Topic 2
Partition 1 Partition 2
Partition 1 Partition 2 Partition 1
Partition 3 Partition 2
Broker 1 Broker 2
Broker 3

57. Apache Kafka
Topic 1
Partition 1 Partition 2
Partition 3
Topic 2
Partition 1 Partition 2
Partition 1 Partition 2
replica
Partition 2
replica
Partition 2 Partition 1
Partition 3
replica
Partition 3 Partition 2
Partition 1
replica
Partition 1
replica
Broker 1 Broker 2
Broker 3

58. How we implemented it?
Topic listingEvents
Partition 1 Partition 2

59. How we implemented it?
ListingHasBeenPublished (id: 1)
Topic listingEvents
Partition 1 Partition 2
ListingHasBeenModified (id: 1)

60. How we implemented it?
ListingHasBeenPublished (id: 1) ListingHasBeenPublished (id: 2)
Topic listingEvents
Partition 1 Partition 2
ListingHasBeenPublished (id: 1)
ListingHasBeenModified (id: 1) ListingHasBeenModified (id: 2)

61. How we implemented it?
ListingHasBeenPublished (id: 1)
ListingHasBeenModified (id: 1)
ListingHasBeenPublished (id: 3)
ListingHasBeenPublished (id: 2)
ListingHasBeenModified (id: 2)
Topic listingEvents
Partition 1 Partition 2

62. How we implemented it?
ListingHasBeenPublished (id: 1)
ListingHasBeenModified (id: 1)
ListingHasBeenPublished (id: 3)
ListingHasBeenModified (id: 1)
ListingHasBeenPublished (id: 2)
ListingHasBeenModified (id: 2)
Topic listingEvents
Partition 1 Partition 2

63. Apache Kafka + Apache Avro
● Send Avro binary records to Kafka
● Every single record has a schema, registered into a
schema registry
● Schema registry allows controlled schema evolution

64. Apache Kafka + Apache Avro
Schema Registry
Kafka Cluster

65. Apache Kafka + Apache Avro
Producer
Schema Registry
Datum A
Datum A
Datum B
Datum B
Kafka Cluster

66. Apache Kafka + Apache Avro
Producer
Schema Registry
Datum A
Datum A
Datum B
Datum B
Kafka Cluster
1 �����
Schema A
1

67. Apache Kafka + Apache Avro
Producer
Schema Registry
Datum A
Datum A
Datum B
Datum B
Kafka Cluster
1 �����
Schema A
1
1 �����

68. Apache Kafka + Apache Avro
Producer
Schema Registry
Datum A
Datum A
Datum B
Datum B
Kafka Cluster
1 �����
Schema A
1
1 �����
Schema B
2
2
2
�����
�����

69. Apache Kafka + Apache Avro
1
1
2
2
�����
�����
�����
�����
Consumer
Producer
Schema A
1
Schema B
2
Schema Registry
Datum A
Datum A
Datum B
Datum B
Kafka Cluster

70. Apache Kafka + Apache Avro
1
1
2
2
�����
�����
�����
�����
Consumer
Producer
Schema A
1
Schema B
2
Schema Registry
Datum A
Datum A
Datum B
Datum B
Kafka Cluster

71. Apache Kafka + Apache Avro
1
1
2
2
�����
�����
�����
�����
Consumer
Producer
Schema A
1
Schema B
2
Schema Registry
Datum A
Datum A
Datum B
Datum B
Kafka Cluster
Datum A

72. Apache Kafka + Apache Avro
1
1
2
2
�����
�����
�����
�����
Consumer
Producer
Schema A
1
Schema B
2
Schema Registry
Datum A
Datum A
Datum B
Datum B
Kafka Cluster
Datum A
Datum A

73. Apache Kafka + Apache Avro
1
1
2
2
�����
�����
�����
�����
Consumer
Producer
Schema A
1
Schema B
2
Schema Registry
Datum A
Datum A
Datum B
Datum B
Kafka Cluster
Datum A
Datum A
Datum B
Datum B

74. Apache Kafka + Apache Avro
1
1
2
2
�����
�����
�����
�����
Consumer
Producer
Schema A
1
Schema B
2
Schema Registry
Datum A
Datum A
Datum B
Datum B
Datum A’
Kafka Cluster
Datum A
Datum A
Datum B
Datum B

75. Apache Kafka + Apache Avro
1
1
2
2
�����
�����
�����
�����
Consumer
Producer
Schema A
1
Schema B
2
Schema Registry
Datum A
Datum A
Datum B
Datum B
Datum A’
3 �����
Schema A’
3
Kafka Cluster
Datum A
Datum A
Datum B
Datum B
Since it’s an evolution, it
will check compatibility.
Fail if broken.

76. Apache Kafka + Apache Avro
1
1
2
2
�����
�����
�����
�����
Consumer
Producer
Schema A
1
Schema B
2
Schema Registry
Datum A
Datum A
Datum B
Datum B
Datum A’
3 �����
Schema A’
3
Kafka Cluster
Datum A
Datum A
Datum B
Datum B
Datum A’
No compatibility broken,
so can read without
problem.

77. How we propagate data?
ListingHasBeenPublished
ListingHasBeenModified
ListingHasBeenHidden
ListingHasBeenModified
ListingHasBeenModified
ListingHasBeenPublished

78. What is LIFULL Connect?
The real-time listings system
How we propagate data?
How we process listings?
How we serve listings?

79. Real-time Listings System

80. Real-time Listings System

81. How we process data?
Apache Kafka Streams

82. ● Framework to do transformations to data stored in
Kafka
● Has an easy-to-use functional API, that runs on JVM
● Does the data processing in streaming
● Ensures exactly once delivery
● Distributed
● Fault tolerant
● Enables stateless and stateful processing of
information
Apache Kafka Streams

83. Apache Kafka Streams
import org.apache.kafka.streams.
KafkaStreams
import org.apache.kafka.streams.
StreamsBuilder
import org.apache.kafka.streams.kstream.
KStream
import java.util.
Properties
fun main() {
val streamsBuilder = StreamsBuilder()
val eventsStream
: KStream<Key, Value> = streamsBuilder
.stream("events")
val transformedStream
: KStream<Key2, Value2> = eventsStream
.map { key, value ->
TODO("do a transformation"
)
}
transformedStream
.to("eventsTransformed"
)
val topology = streamsBuilder
.build()
val kafkaStreams = KafkaStreams(
topology, Properties())
kafkaStreams
.start()
}
Stateless Kafka Streams Application
events
eventsTransformed

84. Apache Kafka Streams
import org.apache.kafka.streams.
KafkaStreams
import org.apache.kafka.streams.
StreamsBuilder
import org.apache.kafka.streams.kstream.
KStream
import java.util.
Properties
fun main() {
val streamsBuilder = StreamsBuilder()
val eventsStream: KStream<Key, Value> = streamsBuilder.stream("events")
val transformedStream: KStream<Key2, Value2> = eventsStream.map
{ key, value ->
TODO("do a transformation")
}
transformedStream.to("eventsTransformed")
val topology = streamsBuilder
.build()
val kafkaStreams = KafkaStreams(
topology, Properties())
kafkaStreams
.start()
}
Stateless Kafka Streams Application
events
eventsTransformed
Boilerplate

85. Apache Kafka Streams
import org.apache.kafka.streams.KafkaStreams
import org.apache.kafka.streams.StreamsBuilder
import org.apache.kafka.streams.kstream.KStream
import java.util.Properties
fun main() {
val streamsBuilder = StreamsBuilder()
val eventsStream
: KStream<Key, Value> = streamsBuilder
.stream("events")
val transformedStream: KStream<Key2, Value2> = eventsStream.map
{ key, value ->
TODO("do a transformation")
}
transformedStream.to("eventsTransformed")
val topology = streamsBuilder.build()
val kafkaStreams = KafkaStreams(topology, Properties())
kafkaStreams.start()
}
Stateless Kafka Streams Application
events
eventsTransformed
Read from a topic

86. Apache Kafka Streams
import org.apache.kafka.streams.KafkaStreams
import org.apache.kafka.streams.StreamsBuilder
import org.apache.kafka.streams.kstream.KStream
import java.util.Properties
fun main() {
val streamsBuilder = StreamsBuilder()
val eventsStream: KStream<Key, Value> = streamsBuilder.stream("events")
val transformedStream
: KStream<Key2, Value2> = eventsStream
.map { key, value ->
TODO("do a transformation"
)
}
transformedStream.to("eventsTransformed")
val topology = streamsBuilder.build()
val kafkaStreams = KafkaStreams(topology, Properties())
kafkaStreams.start()
}
Stateless Kafka Streams Application
events
eventsTransformed
Do a transformation

87. Apache Kafka Streams
import org.apache.kafka.streams.KafkaStreams
import org.apache.kafka.streams.StreamsBuilder
import org.apache.kafka.streams.kstream.KStream
import java.util.Properties
fun main() {
val streamsBuilder = StreamsBuilder()
val eventsStream: KStream<Key, Value> = streamsBuilder.stream("events")
val transformedStream: KStream<Key2, Value2> = eventsStream.map
{ key, value ->
TODO("do a transformation")
}
transformedStream
.to("eventsTransformed"
)
val topology = streamsBuilder.build()
val kafkaStreams = KafkaStreams(topology, Properties())
kafkaStreams.start()
}
Stateless Kafka Streams Application
events
eventsTransformed
Write to a topic

88. Apache Kafka Streams
import org.apache.kafka.streams.
KafkaStreams
import org.apache.kafka.streams.
StreamsBuilder
import org.apache.kafka.streams.kstream.
KStream
import java.util.
Properties
fun main() {
val streamsBuilder = StreamsBuilder()
val eventsStream
: KStream<Key, Value> = streamsBuilder
.stream("events")
val transformedStream
: KStream<Key2, Value2> = eventsStream
.map { key, value ->
TODO("do a transformation"
)
}
transformedStream
.to("eventsTransformed"
)
val topology = streamsBuilder
.build()
val kafkaStreams = KafkaStreams(
topology, Properties())
kafkaStreams
.start()
}
Stateless Kafka Streams Application
events
eventsTransformed

89. Apache Kafka Streams
events
eventsAggregated
import org.apache.kafka.streams.
KafkaStreams
import org.apache.kafka.streams.
StreamsBuilder
import org.apache.kafka.streams.kstream.
KGroupedStream
import org.apache.kafka.streams.kstream.
KStream
import org.apache.kafka.streams.kstream.
KTable
import java.util.
Properties
fun main() {
val streamsBuilder = StreamsBuilder()
val eventsStream
: KStream<Key, Value> = streamsBuilder
.stream("events")
val aggregated
: KTable<Key, Value2> = grouped.groupByKey()
.aggregate(
{
TODO("provide an initial value"
)
}
) { key, value, aggregatedValue ->
TODO("do an aggregation"
)
}
aggregated
.to("eventsAggregated"
)
val topology = streamsBuilder
.build()
val kafkaStreams = KafkaStreams(
topology, Properties())
kafkaStreams
.start()
}
Stateful Kafka Streams Application

90. Apache Kafka Streams
events
eventsAggregated
import org.apache.kafka.streams.KafkaStreams
import org.apache.kafka.streams.StreamsBuilder
import org.apache.kafka.streams.kstream.KGroupedStream
import org.apache.kafka.streams.kstream.KStream
import org.apache.kafka.streams.kstream.KTable
import java.util.Properties
fun main() {
val streamsBuilder = StreamsBuilder()
val eventsStream
: KStream<Key, Value> = streamsBuilder
.stream("events")
val aggregated: KTable<Key, Value2> = grouped
.groupByKey()
.aggregate(
{
TODO("provide an initial value")
}
) { key, value, aggregatedValue ->
TODO("do an aggregation")
}
aggregated.
to("eventsAggregated")
val topology = streamsBuilder.build()
val kafkaStreams = KafkaStreams(topology, Properties())
kafkaStreams.start()
}
Stateful Kafka Streams Application
Read a topic

91. Apache Kafka Streams
mem
store
events
eventsAggregated
import org.apache.kafka.streams.KafkaStreams
import org.apache.kafka.streams.StreamsBuilder
import org.apache.kafka.streams.kstream.KGroupedStream
import org.apache.kafka.streams.kstream.KStream
import org.apache.kafka.streams.kstream.KTable
import java.util.Properties
fun main() {
val streamsBuilder = StreamsBuilder()
val eventsStream: KStream<Key, Value> = streamsBuilder.stream("events")
val aggregated
: KTable<Key, Value2> = grouped.groupByKey()
.aggregate(
{
TODO("provide an initial value"
)
}
) { key, value, aggregatedValue ->
TODO("do an aggregation"
)
}
aggregated.
to("eventsAggregated")
val topology = streamsBuilder.build()
val kafkaStreams = KafkaStreams(topology, Properties())
kafkaStreams.start()
}
Stateful Kafka Streams Application
Do an aggregation

92. Apache Kafka Streams
mem
store
events
eventsAggregated
import org.apache.kafka.streams.KafkaStreams
import org.apache.kafka.streams.StreamsBuilder
import org.apache.kafka.streams.kstream.KGroupedStream
import org.apache.kafka.streams.kstream.KStream
import org.apache.kafka.streams.kstream.KTable
import java.util.Properties
fun main() {
val streamsBuilder = StreamsBuilder()
val eventsStream: KStream<Key, Value> = streamsBuilder.stream("events")
val aggregated: KTable<Key, Value2> = grouped
.groupByKey()
.aggregate(
{
TODO("provide an initial value")
}
) { key, value, aggregatedValue ->
TODO("do an aggregation")
}
aggregated
.to("eventsAggregated"
)
val topology = streamsBuilder.build()
val kafkaStreams = KafkaStreams(topology, Properties())
kafkaStreams.start()
}
Stateful Kafka Streams Application
Write to a topic

93. Apache Kafka Streams
mem
store
events
eventsAggregated
import org.apache.kafka.streams.
KafkaStreams
import org.apache.kafka.streams.
StreamsBuilder
import org.apache.kafka.streams.kstream.
KGroupedStream
import org.apache.kafka.streams.kstream.
KStream
import org.apache.kafka.streams.kstream.
KTable
import java.util.
Properties
fun main() {
val streamsBuilder = StreamsBuilder()
val eventsStream
: KStream<Key, Value> = streamsBuilder
.stream("events")
val aggregated
: KTable<Key, Value2> = grouped.groupByKey()
.aggregate(
{
TODO("provide an initial value"
)
}
) { key, value, aggregatedValue ->
TODO("do an aggregation"
)
}
aggregated
.to("eventsAggregated"
)
val topology = streamsBuilder
.build()
val kafkaStreams = KafkaStreams(
topology, Properties())
kafkaStreams
.start()
}
Stateful Kafka Streams Application

94. How we implemented it?
mem
store
Topic listingEvents Topic listings

95. How we implemented it?
mem
store
Topic listingEvents Topic listings
ListingHasBeenPublished (id: 1)

96. How we implemented it?
mem
store
Topic listingEvents Topic listings
ListingHasBeenPublished (id: 1) Listing (id: 1)

97. How we implemented it?
mem
store
Topic listingEvents Topic listings
ListingHasBeenPublished (id: 1) Listing (id: 1)
ListingHasBeenModified (id: 1)

98. How we implemented it?
mem
store
Topic listingEvents Topic listings
ListingHasBeenPublished (id: 1) Listing (id: 1)
ListingHasBeenModified (id: 1) Listing’ (id: 1)

99. How we implemented it?
mem
store
Topic listingEvents Topic listings
ListingHasBeenPublished (id: 1) Listing (id: 1)
ListingHasBeenModified (id: 1) Listing’ (id: 1)
ListingHasBeenPublished (id: 2) Listing (id: 2)

100. How we implemented it?
ListingHasBeenPublished (id: 1)
ListingHasBeenModified (id: 1)
ListingHasBeenPublished (id: 2)
ListingHasBeenModified (id: 1)
ListingHasBeenModified (id: 2)
ListingHasBeenPublished (id: 3)
Listing (id: 1)
Listing’ (id: 1)
Listing (id: 2)
Listing’’ (id: 1)
Listing’ (id: 2)
Listing (id: 3)
mem
store
Topic listingEvents Topic listings

101. What is LIFULL Connect?
The real-time listings system
How we propagate data?
How we process listings?
How we serve listings?

102. How we serve listings?

103. How we serve listings?

104. How we implemented it?
Apache Kafka Connect

105. Kafka Connect
● Software to connect Kafka data to external systems
● Move data in Kafka with source connectors
● Move data out of Kafka with sink connectors
● Pluggable with third party or custom connectors

106. Kafka Connect
{
"name": "source-connector"
,
"config": {
"connector.class"
: "io.confluent.connect.jdbc.JdbcSourceConnector"
,
"connection.url"
: "jdbc:mysql://mysql:3306/test"
,
"connection.user"
: "connect_user"
,
"connection.password"
: "connect_password"
,
"topic.prefix"
: "mysql-01-"
,
"poll.interval.ms" : 3600000,
"table.whitelist" : "table",
"mode":"bulk"
}
}
Source Connector

107. Kafka Connect
{
"name": "sink-connector"
,
"config": {
"connector.class"
: "io.confluent.connect.elasticsearch.ElasticsearchSinkConnector"
,
"tasks.max"
: "10",
"topics": "realEstateListings"
,
"key.ignore"
: "false",
"batch.size"
: 4000,
"max.buffered.records"
: 20000,
"behavior.on.malformed.documents"
: "warn",
"key.converter"
: "org.apache.kafka.connect.storage.StringConverter"
,
"value.converter"
: "org.apache.kafka.connect.json.JsonConverter"
,
"value.converter.schemas.enable"
: false,
"connection.url"
: "https://elasticsearch-host:443"
}
}
Sink Connector

108. How we implemented it?
Topic listings

109. How we implemented it?
Topic listings
Listing (id: 1)

110. How we implemented it?
Topic listings
Listing (id: 1)
Listing (id: 1)

111. How we implemented it?
Topic listings
Listing (id: 1)
Listing (id: 1)
Listing’ (id: 1)

112. How we implemented it?
Topic listings
Listing (id: 1)
Listing’ (id: 1)
Listing’ (id: 1)

113. How we implemented it?
Topic listings
Listing (id: 1)
Listing’ (id: 1)
Listing’ (id: 1)
Listing (id: 2)

114. How we implemented it?
Topic listings
Listing (id: 1)
Listing’ (id: 1)
Listing’ (id: 1)
Listing (id: 2)
Listing (id: 2)

115. How we implemented it?
Listing (id: 1)
Listing’ (id: 1)
Listing (id: 2)
Listing’’ (id: 1)
Listing’ (id: 2)
Listing (id: 3)
Listing’’ (id: 1)
Listing’ (id: 2)
Listing (id: 3)
Topic listings

116. How we implemented it?
Listing (id: 1)
Listing’ (id: 1)
Listing (id: 2)
Listing’’ (id: 1)
Listing’ (id: 2)
Listing (id: 3)
Listing’’ (id: 1)
Listing’ (id: 2)
Listing (id: 3)
API
Topic listings

117. How we implemented it?
Listing (id: 1)
Listing’ (id: 1)
Listing (id: 2)
Listing’’ (id: 1)
Listing’ (id: 2)
Listing (id: 3)
🌎
🌍
🌏
Topic listings
API
API
API

118. What is LIFULL Connect?
The real-time listings system
How we propagate data?
How we process listings?
How we serve listings?
Recap

119. Recap

120. To-do list
● Better observability
● Conventions with Public vs Private topics
● Proper data catalog
● Other divisions using Kafka, avoiding the caveats of a
multi-tenant cluster
● …

121. Potential unlocked
We do more than just serve
listings
Like, adapt the listing depending on its popularity, in real-time!

122. WE
HELP PEOPLE
FIND A HOME
Keep following our mission

123. OK THANKS BYE
Want to know more?
See you at LIFULL Connect stand
@ferrangali
@LifullConnEng

124. Questions?