🚂 On Track with Apache Kafka: Building a Streaming ETL solution with Rail Data

A presentation at Apache Kafka Meetup by Robin Moffatt

https://wiki.openraildata.com @rmoff | @confluentinc

ig nf Co Co nf ig SQL @rmoff | @confluentinc

Real-time visualisation of rail data @rmoff | @confluentinc

Real-time visualisation of rail data O M E D @rmoff | @confluentinc

Code! http://rmoff.dev/kafka-trains-code-01 @rmoff | @confluentinc

Graph relationships @rmoff | @confluentinc

Getting the data in 📥 @rmoff | @confluentinc

Data Sources Train Movements Continuous feed Schedules Updated Daily Reference data Static @rmoff | @confluentinc

Streaming Integration with Kafka Connect Amazon syslog Google Kafka Connect Kafka Brokers @rmoff | @confluentinc

kafkacat • Kafka CLI producer & consumer • Also metadata inspection • Integrates beautifully with unix pipes! curl -s “https://my.api.endpoint” | \ kafkacat -b localhost:9092 -t topic -P • Get it! • https://github.com/edenhill/kafkacat/ • apt-get install kafkacat • brew install kafkacat • https://hub.docker.com/r/confluentinc/cp-kafkacat/ https://rmoff.net/2018/05/10/quick-n-easy-population-of-realistic-test-data-into-kafka/ @rmoff | @confluentinc

https://wiki.openraildata.com/index.php?title=Train_Movements Movement data A train’s movements Arrived / departed Where What time Variation from timetable A train was cancelled Where When Why A train was ‘activated’ Links a train to a schedule @rmoff | @confluentinc

Ingest ActiveMQ with Kafka Connect “connector.class” : “activemq.url” : “jms.destination.type”: “jms.destination.name”: “kafka.topic” : “io.confluent.connect.activemq.ActiveMQSourceConnector”, “tcp://datafeeds.networkrail.co.uk:61619”, “topic”, “TRAIN_MVT_EA_TOC”, “networkrail_TRAIN_MVT”, Kafka Movement events Kafka Connect Northern Trains GNER* TransPennine networkrail_TRAIN_MVT
Remember them? @rmoff | @confluentinc

Message transformation Extract element Convert JSON Explode Array kafkacat -b localhost:9092 -G tm_explode networkrail_TRAIN_MVT -u | \ jq -c ‘.text|fromjson[]’ | \ kafkacat -b localhost:9092 -t networkrail_TRAIN_MVT_X -T -P @rmoff | @confluentinc

https://wiki.openraildata.com/index.php?title=SCHEDULE Schedule data Train routes Type of train (diesel/electric) Classes, sleeping accomodation, reservations, etc @rmoff | @confluentinc

Load JSON from S3 into Kafka curl -s -L -u “$USER:$PW” “$FEED_URL” | \ gunzip | \ kafkacat -b localhost -P -t CIF_FULL_DAILY @rmoff | @confluentinc

https://wiki.openraildata.com/index.php?title=SCHEDULE Reference data Train company names Location codes Train type codes Location geocodes @rmoff | @confluentinc

Static reference data @rmoff | @confluentinc

AA:{“canx_reason_code”:”AA”,”canx_reason”:”Waiting acceptance into off Network Terminal or Yard”} AC:{“canx_reason_code”:”AC”,”canx_reason”:”Waiting train preparation or completion of TOPS list/RT3973”} AE:{“canx_reason_code”:”AE”,”canx_reason”:”Congestion in off Network Terminal or Yard”} AG:{“canx_reason_code”:”AG”,”canx_reason”:”Wagon load incident including adjusting loads or open door”} AJ:{“canx_reason_code”:”AJ”,”canx_reason”:”Waiting Customer’s traffic including documentation”} DA:{“canx_reason_code”:”DA”,”canx_reason”:”Non Technical Fleet Holding Code”} DB:{“canx_reason_code”:”DB”,”canx_reason”:”Train Operations Holding Code”} DC:{“canx_reason_code”:”DC”,”canx_reason”:”Train Crew Causes Holding Code”} DD:{“canx_reason_code”:”DD”,”canx_reason”:”Technical Fleet Holding Code”} DE:{“canx_reason_code”:”DE”,”canx_reason”:”Station Causes Holding Code”} DF:{“canx_reason_code”:”DF”,”canx_reason”:”External Causes Holding Code”} DG:{“canx_reason_code”:”DG”,”canx_reason”:”Freight Terminal and or Yard Holding Code”} DH:{“canx_reason_code”:”DH”,”canx_reason”:”Adhesion and or Autumn Holding Code”} FA:{“canx_reason_code”:”FA”,”canx_reason”:”Dangerous goods incident”} FC:{“canx_reason_code”:”FC”,”canx_reason”:”Freight train driver”} kafkacat -l canx_reason_code.dat -b localhost:9092 -t canx_reason_code -P -K: @rmoff | @confluentinc

Transforming the data @rmoff | @confluentinc

Movement Activation Schedule Location @rmoff | @confluentinc

Streams of events Time @rmoff | @confluentinc

Stream Processing with ksqlDB Stream: widgets Stream: widgets_red @rmoff | @confluentinc

Stream Processing with ksqlDB Stream: widgets CREATE STREAM widgets_red AS SELECT * FROM widgets WHERE colour=’RED’; Stream: widgets_red @rmoff | @confluentinc

Event data ActiveMQ Kafka Connect NETWORKRAIL_TRAIN_MVT_X @rmoff | @confluentinc

Message routing with ksqlDB TRAIN_ACTIVATIONS Time NETWORKRAIL_TRAIN_MVT_X ’ 1 0 00 ’ = E YP T _ G MS TRAIN_CANCELLATIONS Time MSG_TYPE=’0002’ Time MSG _TY PE= ‘00 03’ TRAIN_MOVEMENTS Time @rmoff | @confluentinc

Split a stream of data (message routing) CREATE STREAM TRAIN_MOVEMENTS_00 AS SELECT * FROM NETWORKRAIL_TRAIN_MVT_X s t n e v e t n e m e ’ v 3 o 0 0 0 M ’ PE= Y T _ SG M WHERE header->msg_type = ‘0003’; NETWORKRAIL_TRAIN_MVT_X Source topic CREATE STREAM TRAIN_CANCELLATIONS_00 AS SELECT * Can cella t MSG _TY FROM NETWORKRAIL_TRAIN_MVT_X ion e v e PE= n t s ‘00 0 2’ WHERE header->msg_type = ‘0002’; @rmoff | @confluentinc

Joining events to lookup data (stream-table joins) TS 10:00:01 LOC_ID 42 TS 10:00:02 LOC_ID 43 TS 10:00:01 LOC_ID 42 LOC_DESC Menston TS 10:00:02 LOC_ID 43 LOC_DESC Ilkley TRAIN_MOVEMENTS (Stream) LOCATION (Table) ID DESCRIPTION 42 MENSTON 43 Ilkley CREATE STREAM TRAIN_MOVEMENTS_ENRICHED AS SELECT TM.LOC_ID AS LOC_ID, TM.TS AS TS, L.DESCRIPTION AS LOC_DESC, […] FROM TRAIN_MOVEMENTS TM LEFT JOIN LOCATION L TRAIN_MOVEMENTS_ENRICHED ON TM.LOC_ID = L.ID; (Stream) @rmoff | @confluentinc

Decode values, and generate composite key +———————-+————————+————-+——————————-+————————+————————————+ | CIF_train_uid | sched_start_dt | stp_ind | SCHEDULE_KEY | CIF_power_type | POWER_TYPE | +———————-+————————+————-+——————————-+————————+————————————+ | Y82535 | 2019-05-20 | P | Y82535/2019-05-20/P | E | Electric | | Y82537 | 2019-05-20 | P | Y82537/2019-05-20/P | DMU | Other | | Y82542 | 2019-05-20 | P | Y82542/2019-05-20/P | D | Diesel | | Y82535 | 2019-05-24 | P | Y82535/2019-05-24/P | EMU | Electric Multiple Unit | | Y82542 | 2019-05-24 | P | Y82542/2019-05-24/P | HST | High Speed Train | SELECT JsonScheduleV1->CIF_train_uid + ‘/’ + JsonScheduleV1->schedule_start_date + ‘/’ + JsonScheduleV1->CIF_stp_indicator AS SCHEDULE_KEY, CASE WHEN JsonScheduleV1->schedule_segment->CIF_power_type = ‘D’ THEN ‘Diesel’ WHEN JsonScheduleV1->schedule_segment->CIF_power_type = ‘E’ THEN ‘Electric’ WHEN JsonScheduleV1->schedule_segment->CIF_power_type = ‘ED’ THEN ‘Electro-Diesel’ END AS POWER_TYPE @rmoff | @confluentinc

Schemas CREATE STREAM SCHEDULE_RAW ( TiplocV1 STRUCT<transaction_type tiploc_code NALCO STANOX crs_code description tps_description WITH (KAFKA_TOPIC=’CIF_FULL_DAILY’, VALUE_FORMAT=’JSON’); @rmoff | VARCHAR, VARCHAR, VARCHAR, VARCHAR, VARCHAR, VARCHAR, VARCHAR>) @confluentinc

Handling JSON data CREATE STREAM NETWORKRAIL_TRAIN_MVT_X ( header STRUCT< msg_type VARCHAR, […] >, body VARCHAR) WITH (KAFKA_TOPIC=’networkrail_TRAIN_MVT_X’, VALUE_FORMAT=’JSON’); CREATE STREAM TRAIN_CANCELLATIONS_00 AS SELECT HEADER, EXTRACTJSONFIELD(body,’$.canx_timestamp’) AS canx_timestamp, EXTRACTJSONFIELD(body,’$.canx_reason_code’) AS canx_reason_code, […] FROM networkrail_TRAIN_MVT_X WHERE header->msg_type = ‘0002’; @rmoff | @confluentinc

The Confluent Schema Registry Avro Schema Schema Registry Target Source ksqlDB Avro Message Avro Message Kafka Connect @rmoff | @confluentinc

Reserialise data CREATE STREAM TRAIN_CANCELLATIONS_00 WITH (VALUE_FORMAT=’AVRO’) AS SELECT * FROM networkrail_TRAIN_MVT_X WHERE header->msg_type = ‘0002’; @rmoff | @confluentinc

Flatten and re-key a stream { CREATE STREAM TIPLOC_FLAT_KEYED SELECT TiplocV1->TRANSACTION_TYPE TiplocV1->TIPLOC_CODE TiplocV1->NALCO TiplocV1->STANOX TiplocV1->CRS_CODE TiplocV1->DESCRIPTION TiplocV1->TPS_DESCRIPTION FROM SCHEDULE_RAW PARTITION BY TIPLOC_CODE; y e e g k n g a n h i C n ksql> DESCRIBE TIPLOC_FLAT_KEYED; io t i art “TiplocV1”: { “transaction_type”: “Create”, “tiploc_code”: “ABWD”, “nalco”: “513100”, “stanox”: “88601”, “crs_code”: “ABW”, “description”: “ABBEY WOOD”, “tps_description”: “ABBEY WOOD” } } p Access nested element AS AS AS AS AS AS AS TRANSACTION_TYPE , TIPLOC_CODE , NALCO , STANOX , CRS_CODE , DESCRIPTION , TPS_DESCRIPTION Name : TIPLOC_FLAT_KEYED Field | Type ——————————————————————-TRANSACTION_TYPE | VARCHAR(STRING) TIPLOC_CODE | VARCHAR(STRING) NALCO | VARCHAR(STRING) STANOX | VARCHAR(STRING) CRS_CODE | VARCHAR(STRING) DESCRIPTION | VARCHAR(STRING) TPS_DESCRIPTION | VARCHAR(STRING) @rmoff | @confluentinc ———————————————————————

Kafka -> Elasticsearch { “connector.class”: “io.confluent.connect.elasticsearch.ElasticsearchSinkConnector”, “topics”: “TRAIN_MOVEMENTS_ACTIVATIONS_SCHEDULE_00”, “connection.url”: “http://elasticsearch:9200”, “type.name”: “type.name=kafkaconnect”, “key.ignore”: “false”, “schema.ignore”: “true”, “key.converter”: “org.apache.kafka.connect.storage.StringConverter” } @rmoff | @confluentinc

Kibana for real-time visualisation of rail data @rmoff | @confluentinc

Kibana for real-time analysis of rail data @rmoff | @confluentinc

Kafka -> Postgres { “connector.class”: “io.confluent.connect.jdbc.JdbcSinkConnector”, “key.converter”: “org.apache.kafka.connect.storage.StringConverter”, “connection.url”: “jdbc:postgresql://postgres:5432/”, “connection.user”: “postgres”, “connection.password”: “postgres”, “auto.create”: true, “auto.evolve”: true, “insert.mode”: “upsert”, “pk.mode”: “record_key”, “pk.fields”: “MESSAGE_KEY”, “topics”: “TRAIN_MOVEMENTS_ACTIVATIONS_SCHEDULE_00”, “transforms”: “dropArrays”, “transforms.dropArrays.type”: “org.apache.kafka.connect.transforms.ReplaceField$Value”, “transforms.dropArrays.blacklist”: “SCHEDULE_SEGMENT_LOCATION, HEADER” } @rmoff | @confluentinc

Kafka -> Postgres @rmoff | @confluentinc

But do you even need a database? TRAIN_MOVEMENTS Time Time SELECT TIMESTAMPTOSTRING(WINDOWSTART(),’yyyy-MM-dd’) AS WINDOW_START_TS, VARIATION_STATUS, SUM(CASE WHEN TOC = ‘Arriva Trains Northern’ THEN 1 ELSE 0 END) AS Arriva_CT, SUM(CASE WHEN TOC = ‘East Midlands Trains’ THEN 1 ELSE 0 END) AS EastMidlands_CT, SUM(CASE WHEN TOC = ‘London North Eastern Railway’ THEN 1 ELSE 0 END) AS LNER_CT, SUM(CASE WHEN TOC = ‘TransPennine Express’ THEN 1 ELSE 0 END) AS TransPennineExpress_CT FROM TRAIN_MOVEMENTS WINDOW TUMBLING (SIZE 1 DAY) GROUP BY VARIATION_STATUS; +——————+——————-+————+—————+———+———-+ | Date | Variation | Arriva | East Mid | LNER | TPE | +——————+——————-+————+—————+———+———-+ | 2019-07-02 | OFF ROUTE | 46 | 78 | 20 | 167 | | 2019-07-02 | ON TIME | 19083 | 3568 | 1509 | 2916 | TOC_STATS | 2019-07-02 | LATE | 30850 | 7953 | 5420 | 9042 | | 2019-07-02 | EARLY | 11478 | 3518 | 1349 | 2096 | Aggregate | 2019-07-03 | OFF ROUTE | 79 | 25 | 41 | 213 | Aggregate | 2019-07-03 | ON TIME | 19512 | 4247 | 1915 | 2936 | | 2019-07-03 | LATE | 37357 | 8258 | 5342 | 11016 | | 2019-07-03 | EARLY | 11825 | 4574 | 1888 | 2094 | @rmoff | @confluentinc

{ Kafka -> S3 “connector.class”: “io.confluent.connect.s3.S3SinkConnector”, “key.converter”:”org.apache.kafka.connect.storage.StringConverter”, “tasks.max”: “1”, “topics”: “TRAIN_MOVEMENTS_ACTIVATIONS_SCHEDULE_00”, “s3.region”: “us-west-2”, “s3.bucket.name”: “rmoff-rail-streaming-demo”, “flush.size”: “65536”, “storage.class”: “io.confluent.connect.s3.storage.S3Storage”, “format.class”: “io.confluent.connect.s3.format.avro.AvroFormat”, “schema.generator.class”: “io.confluent.connect.storage.hive.schema.DefaultSchemaGenerator”, “schema.compatibility”: “NONE”, “partitioner.class”: “io.confluent.connect.storage.partitioner.TimeBasedPartitioner”, “path.format”:“‘year’=YYYY/’month’=MM/’day’=dd”, “timestamp.extractor”:”Record”, “partition.duration.ms”: 1800000, “locale”:”en”, “timezone”: “UTC” } @rmoff | @confluentinc

Kafka -> S3 $ aws s3 ls s3://rmoff-rail-streaming-demo/topics/TRAIN_MOVEMENTS_ACTIVATIONS_SCHEDULE_00/year=2019/month=07/day=07/ 2019-07-07 10:05:41 200995548 TRAIN_MOVEMENTS_ACTIVATIONS_SCHEDULE_00+0+0004963416.avro 2019-07-07 19:47:11 87631494 TRAIN_MOVEMENTS_ACTIVATIONS_SCHEDULE_00+0+0005007151.avro @rmoff | @confluentinc

Analysis with AWS Athena @rmoff | @confluentinc

Graph analysis { “connector.class”: “streams.kafka.connect.sink.Neo4jSinkConnector”, “topics”: “TRAIN_CANCELLATIONS_02”, “key.converter”:”org.apache.kafka.connect.storage.StringConverter”, “errors.tolerance”: “all”, “errors.deadletterqueue.topic.name”: “sink-neo4j-train-00_dlq”, “errors.deadletterqueue.topic.replication.factor”: 1, “errors.deadletterqueue.context.headers.enable”:true, “neo4j.server.uri”: “bolt://neo4j:7687”, “neo4j.authentication.basic.username”: “neo4j”, “neo4j.authentication.basic.password”: “connect”, “neo4j.topic.cypher.TRAIN_CANCELLATIONS_02”: “MERGE (train:train{id: event.TRAIN_ID}) MERGE (toc:toc{toc: event.TOC}) MERGE (canx_reason:canx_reason{reason: event.CANX_REASON}) MERGE (canx_loc:canx_loc{location: coalesce(event.CANCELLATION_LOCATION,’<unknown>’)}) MERGE (train)[:OPERATED_BY]->(toc) MERGE (canx_loc)<-[:CANCELLED_AT{reason:event.CANX_REASON, time:event.CANX_TIMESTAMP}]-(train)-[:CANCELLED_BECAUSE]->(canx_reason)” } @rmoff | @confluentinc

Tell me when trains are delayed at my station @rmoff | @confluentinc

Event-driven Alerting - logic TRAIN_MOVEMENTS Time TRAIN_DELAYED_ALERT ALERT_CONFIG Key/Value state Time CREATE STREAM TRAINS_DELAYED_ALERT AS SELECT […] FROM TRAIN_MOVEMENTS T INNER JOIN ALERT_CONFIG A ON T.LOC_NLCDESC = A.STATION WHERE TIMETABLE_VARIATION > A.ALERT_OVER_MINS; @rmoff | @confluentinc

Sending messages to Telegram curl -X POST -H ‘Content-Type: application/json’ -d ‘{“chat_id”: “-364377679”, “text”: “This is a test from curl”}’ https://api.telegram.org/botxxxxx/sendMessage { “connector.class”: “io.confluent.connect.http.HttpSinkConnector”, “request.method”: “post”, “http.api.url”: “https://api.telegram.org/[…]/sendMessage”, “headers”: “Content-Type: application/json”, “topics”: “TRAINS_DELAYED_ALERT_TG”, “tasks.max”: “1”, “batch.prefix”: “{“chat_id”:”-364377679”,”parse_mode”: “markdown”,”, “batch.suffix”: “}”, “batch.max.size”: “1”, “regex.patterns”:”.\{MESSAGE=(.)\}.*”, “regex.replacements”: “”text”:”$1”“, “regex.separator”: “~”, “key.converter”: “org.apache.kafka.connect.storage.StringConverter” } Kafka Kafka Connect HTTP Sink Telegram @rmoff | @confluentinc

Running it & monitoring & maintenance @rmoff | @confluentinc

System health & Throughput @rmoff | @confluentinc

Configuring and monitoring Kafka Connect @rmoff | @confluentinc

@rmoff @confluentinc Restart failing connectors rmoff@proxmox01 > crontab -l “*/5 * * * * /home/rmoff/restart_failed_connector_tasks.sh rmoff@proxmox01 > cat /home/rmoff/restart_failed_connector_tasks.sh “#!/usr/bin/env bash # @rmoff / June 6, 2019
Restart any connector tasks that are FAILED curl -s “http:”//localhost:8083/connectors” | \ jq ‘.[]’ | \ xargs -I{connector_name} curl -s “http:”//localhost:8083/connectors/”{connector_name}”/status” | \ jq -c -M ‘[select(.tasks[].state”==”FAILED”) | .name,”§±§”,.tasks[].id]’ | \ grep -v “[]”| \ sed -e ‘s/^[“”//g’| sed -e ‘s/”,”§±§”,//tasks”//g’|sed -e ‘s/]$”//g’| \ xargs -I{connector_and_task} curl -X POST “http:”//localhost:8083/connectors/”{connector_and_task}”/ restart”
🚂 On Track with Apache Kafka: Building a Streaming ETL solution with Rail Data

Is everything running? @rmoff @confluentinc http://rmoff.dev/kafka-trains-code-01 🚂 On Track with Apache Kafka: Building a Streaming ETL solution with Rail Data

@rmoff @confluentinc check_latest_timestamp.sh Start at latest Just read one message message kafkacat -b localhost:9092 -t networkrail_TRAIN_MVT_X -o-1 -c1 -C | \ jq ‘.header.msg_queue_timestamp’ | \ sed -e ‘s/”“//g’ | \ sed -e ‘s/000$”//g’ | \ Convert from epoch to humanxargs -Ifoo date “—date=@foo readable timestamp format $ ./check_latest_timestamp.sh Mon 25 Jan 2021 13:39:40 GMT 🚂 On Track with Apache Kafka: Building a Streaming ETL solution with Rail Data

Kafka Connect Kafka Kafka Connect ksqlDB ✅ Stream raw data ✅ Stream enriched data ✅ Historic data & data replay @rmoff | @confluentinc

RM OF F2 00 $200 USD off your bill each calendar month for the first three months when you sign up https://rmoff.dev/ccloud Free money! (additional $200 towards your bill 😄 ) Fully Managed Kafka as a Service * T&C: https://www.confluent.io/confluent-cloud-promo-disclaimer

Learn Kafka. Start building with Apache Kafka at Confluent Developer. developer.confluent.io

Robin Moffatt
@rmoff

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As data engineers, we frequently need to build scalable systems working with data from a variety of sources and with various ingest rates, sizes, and formats. This talk takes an in-depth look at how Apache Kafka can be used to provide a common platform on which to build data infrastructure driving both real-time analytics as well as event-driven applications.

Using a public feed of railway data it will show how to ingest data from message queues such as ActiveMQ with Kafka Connect, as well as from static sources such as S3 and REST endpoints. We’ll then see how to use stream processing to transform the data into a form useful for streaming to analytics in tools such as Elasticsearch and Neo4j. The same data will be used to drive a real-time notifications service through Telegram.

If you’re wondering how to build your next scalable data platform, how to reconcile the impedance mismatch between stream and batch, and how to wrangle streams of data—this talk is for you!

Resources

The following resources were mentioned during the presentation or are useful additional information.

🎥 Recording
☁️Confluent Cloud

Fully Managed Apache Kafka, Schema Registry, ksqlDB, and Connectors.
- Use promo code RMOFF200 to get an additional $200 of free Confluent Cloud usage. Be sure to activate it by June 30 2021, and to use it within 90 days after activation. Any unused promo value on the expiration date will be forfeited and there are a limited number of codes available, so don’t miss out!
📌 Kafka as a Platform: the Ecosystem from the Ground Up
🎥 Kafka Connect tutorials on YouTube

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First #streamingselfie of the year :) There’s still time to join the Meetup now if you want: https://t.co/nmrjj3WDmG pic.twitter.com/5dX0CLciEX
— Robin Moffatt 🍻🏃🥓 (@rmoff) January 26, 2021
Thanks, really enjoyed the talk. And as I was working on public transport data recently I could relate a lot.
— Bert (@taseroth) January 26, 2021
@rmoff Good talk, thanks!
— E.J. Noordhuis (@ejnoordhuis) January 26, 2021
Great talk and thanks for sharing
— Franziska Kühn (@FranziskaKhn4) January 26, 2021

🚂 On Track with Apache Kafka: Building a Streaming ETL solution with Rail Data

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Resources

🎥 Recording

☁️Confluent Cloud

📌 Kafka as a Platform: the Ecosystem from the Ground Up

🎥 Kafka Connect tutorials on YouTube

Buzz and feedback