The document discusses Gimel, a big data framework developed by PayPal, which aims to streamline data access and simplify the data application lifecycle. It highlights the challenges faced in data processing and access, proposing solutions through a unified data API and catalog that can handle multiple storage backends. The document also outlines future enhancements for Gimel, including expanded cloud support and optimization strategies.

1. Unified Data Access with Gimel
Vladimir Bacvanski
Anisha Nainani
Deepak Chandramouli

2. About us
Vladimir Bacvanski
vbacvanski@paypal.com
Twitter: @OnSoftware
• Principal Architect, Strategic Architecture at PayPal
• In previous life: CTO of a development and
consulting firm
• PhD in Computer Science from RWTH Aachen,
Germany
• O’Reilly author: Courses on Big Data, Kafka
Deepak Chandramouli
dmohanakumarchan@paypal.com
LinkedIn: @deepakmc
• MT2 Software Engineer, Data Platform Services at
PayPal
• Data Enthusiast
• Tech lead
• Gimel (Big Data Framework for Apache Spark)
• Unified Data Catalog – PayPal’s Enterprise Data
Catalog
Anisha Nainani
annainani@paypal.com
LinkedIn: @anishanainani
• Senior Software Engineer
• Big Data
• Data Platform Services

3. AGENDA
❑ PayPal - Introduction
❑ Why Gimel
❑ Gimel Deep Dive
❑ What’s next?
❑ Questions

4. PayPal – Key Metrics and Analytics Ecosystem
4

5. PayPal | Q3-2020 | Key Metrics
5https://investor.pypl.com/home/default.aspx

6. PayPal | Data Growth
6
160+ PB Data200,000+
YARN jobs/day
One of the largest
Aerospike,
Teradata,
Hortonworks
and Oracle
installations
Compute
supported:
Spark, Hive,
MR, BigQuery
20+ On-Premise
clusters
GPU co-located with
Hadoop
Cloud Migration
Adjacencies

7. 7
Developer Data scientist Analyst Operator
Gimel
SDK
Notebooks
UDC Data API
Infrastructure services leveraged for elasticity and redundancy
Multi-DC Public cloudPredictive resource allocation
Logging
Monitoring
Alerting
Security
Application
Lifecycle
Management
Compute
Frameworkand
APIs
GimelData
Platform
User
Experience
andAccess
R Studio BI tools
PayPal | Data Landscape

8. Why Gimel?

9. 9
Challenges | Data Access Code | Cumbersome & Fragile
Spark Read From Hbase Spark Read From Elastic
Search
Spark Read From AeroSpike Spark Read From Druid
Illustration Purpose
Not Meant to Read
Spark Read From Hbase

10. 10
Challenges | Data Processing Can Be Multi-Mode & Polyglot
Batch

11. 11
Challenges with Data App Lifecycle
Learn Code Optimize Build Deploy RunOnboarding Big Data Apps
Learn Code Optimize Build Deploy RunCompute Version Upgraded
Learn Code Optimize Build Deploy RunStorage API Changed
Learn Code Optimize Build Deploy RunStorage Connector Upgraded
Learn Code Optimize Build Deploy RunStorage Hosts Migrated
Learn Code Optimize Build Deploy RunStorage Changed
Learn Code Optimize Build Deploy Run*********************

12. 12
Gimel Simplifies Data Application Lifecycle
Data Application Lifecycle - With Data API
Learn Code Optimize Build Deploy RunOnboarding Big Data Apps
Compute Version Upgraded
Storage API Changed
Storage Connector Upgraded
Storage Hosts Migrated
Storage Changed
*********************
Run
Run
Run
Run
Run
Run

13. 13
Challenges | Instrumentation Required at multiple touchpoints
Catalog /
Classification
Platform Centric
Interceptors
id name address
1 XXXX XXXX
2 XXXX XXXX
Visibility
Security
Data User /
App
Data Stores

14. 14
Putting it all together…
id First_name Last_name address
1 XXXX XXXX XXXX
2 XXXX XXXX XXXX
3 XXXX XXXX XXXX
Data
User
Data App
Data Stores
Catalog /
Classification
Alert
Platform Centric
InterceptorsSecurity
Data / SQL API
App App App
App
…
….

15. 15
Query Routing – Concept
15
Spark / Gimel
Application
Notebooks
Developer/Analyst/Data
Scientist
User / App needs transaction data
• NRT (Streaming)
• 7 days (Analytics Cache)
• 2 Years (cold storage)
1. Submits query to
GSQL Kernel
2. Submits
query to GTS Where txn_dt =
last_7_days
Fast Access Via Cache
A
P
P
• Gimel looks at logical dataset
in UDC
• Interpret filter criteria and
route query to appropriate
storage
Where txn_dt =
last_30_mins
Where txn_dt =
last_2_years

16. 16
Query Routing – Concept
16
• Alluxio Caching – fast access to remote data
• Enterprise Catalog Service – provides config for various stores
• Logical Dataset in catalog - abstracts multiple target stores underneath
• Query pattern (filter) based routing – provides ability to serve data dynamically

17. Code

18. Code Base
Docs
http://gimel.io
Code
Gimel_Notebook
Github
https://github.com/paypal/gimel
Gitter
https://gitter.im/paypal/gimel_data_api_community

19. Gimel – Deep Dive
Peek into implementation
19

20. Unified Data API & SQL Abstraction
20
With Data APISpark Read From Hbase
Spark Read From Elastic
Search
With SQL

21. Unified Data API & Unified Config
Unified Data API
Unified Connector Config

22. Set gimel.catalog.provider=UDC
CatalogProvider.getDataSetProperties(“dataSetName”)
Metadata
Services
Set gimel.catalog.provider=USER
CatalogProvider.getDataSetProperties(“dataSetName”)
Set gimel.catalog.provider=HIVE
CatalogProvider.getDataSetProperties(“dataSetName”)
sql> set dataSetProperties={
"key.deserializer":"org.apache.kafka.common.serialization.StringDeserializer",
"auto.offset.reset":"earliest",
"gimel.kafka.checkpoint.zookeeper.host":"zookeeper:2181",
"gimel.storage.type":"kafka",
"gimel.kafka.whitelist.topics":"kafka_topic",
"datasetName":"test_table1",
"value.deserializer":"org.apache.kafka.common.serialization.ByteArrayDeserializer
",
"value.serializer":"org.apache.kafka.common.serialization.ByteArraySerializer",
"gimel.kafka.checkpoint.zookeeper.path":"/pcatalog/kafka_consumer/checkpoint",
"gimel.kafka.avro.schema.source":"CSR",
"gimel.kafka.zookeeper.connection.timeout.ms":"10000",
"gimel.kafka.avro.schema.source.url":"http://schema_registry:8081",
"key.serializer":"org.apache.kafka.common.serialization.StringSerializer",
"gimel.kafka.avro.schema.source.wrapper.key":"schema_registry_key",
"gimel.kafka.bootstrap.servers":"localhost:9092"
}
sql> Select * from pcatalog.test_table1.
spark.sql("set gimel.catalog.provider=USER");
val dataSetOptions = DataSetProperties(
"KAFKA",
Array(Field("payload","string",true)) ,
Array(),
Map(
"datasetName" -> "test_table1",
"auto.offset.reset"-> "earliest",
"gimel.kafka.bootstrap.servers"-> "localhost:9092",
"gimel.kafka.avro.schema.source"-> "CSR",
"gimel.kafka.avro.schema.source.url"-> "http://schema_registry:8081",
"gimel.kafka.avro.schema.source.wrapper.key"-> "schema_registry_key",
"gimel.kafka.checkpoint.zookeeper.host"-> "zookeeper:2181",
"gimel.kafka.checkpoint.zookeeper.path"->
"/pcatalog/kafka_consumer/checkpoint",
"gimel.kafka.whitelist.topics"-> "kafka_topic",
"gimel.kafka.zookeeper.connection.timeout.ms"-> "10000",
"gimel.storage.type"-> "kafka",
"key.serializer"-> "org.apache.kafka.common.serialization.StringSerializer",
"value.serializer"-> "org.apache.kafka.common.serialization.ByteArraySerializer"
)
)
dataSet.read(”test_table1",Map("dataSetProperties"->dataSetOptions))
CREATE EXTERNAL TABLE `pcatalog.test_table1`
(payload string)
LOCATION 'hdfs://tmp/'
TBLPROPERTIES (
"datasetName" -> "dummy",
"auto.offset.reset"-> "earliest",
"gimel.kafka.bootstrap.servers"-> "localhost:9092",
"gimel.kafka.avro.schema.source"-> "CSR",
"gimel.kafka.avro.schema.source.url"-> "http://schema_registry:8081",
"gimel.kafka.avro.schema.source.wrapper.key"-> "schema_registry_key",
"gimel.kafka.checkpoint.zookeeper.host"-> "zookeeper:2181",
"gimel.kafka.checkpoint.zookeeper.path"->
"/pcatalog/kafka_consumer/checkpoint",
"gimel.kafka.whitelist.topics"-> "kafka_topic",
"gimel.kafka.zookeeper.connection.timeout.ms"-> "10000",
"gimel.storage.type"-> "kafka",
"key.serializer"-> "org.apache.kafka.common.serialization.StringSerializer",
"value.serializer"->
"org.apache.kafka.common.serialization.ByteArraySerializer"
);
Spark-sql> Select * from pcatalog.test_table1
Scala>
dataSet.read(”test_table1",Map("dataSetProperties"->dataSetOptions))
Anatomy of Catalog Provider
Metadata
Set gimel.catalog.provider=YOUR_CATALOG
CatalogProvider.getDataSetProperties(“dataSetName”)
{
// Implement this !
}

23. gimel.dataset.factory {
KafkaDataSet
ElasticSearchDataSet
DruidDataSet
HiveDataSet
AerospikeDataSet
HbaseDataSet
CassandraDataSet
JDBCDataSet
}
Metadata
Services
dataSet.read(“dataSetName”,options)
dataSet.write(dataToWrite,”dataSetName”, options)
dataStream.read(“dataSetName”, options)
val storageDataSet = getFromFactory(type=“Hive”)
{
Core Connector Implementation, example – Kafka
Combination of Open Source Connector and
In-house implementations
Open source connector such as DataStax / SHC /
ES-Spark
}
Anatomy of API
gimel.datastream.factory {
KafkaDataStream
}
CatalogProvider.getDataSetProperties(“dataSetName”)
val storageDataStream = getFromStreamFactory(type=“kafka”)
kafkaDataSet.read(“dataSetName”,options)
hiveDataSet.write(dataToWrite,”dataSetName”, options)
storageDataStream.read(“dataSetName”, options)
dataSet.write(”pcatalog.HIVE_dataset”,readDf , options)
val dataSet : gimel.DataSet = DataSet(sparkSession)
val df1 = dataSet.read(“pcatalog.KAFKA_dataset”, options);
df1.createGlobalTempView(“tmp_abc123”)
Val resolvedSelectSQL =
selectSQL.replace(“pcatalog.KAFKA_dataset”,”tmp_abc123”)
Val readDf : DataFrame = sparkSession.sql(resolvedSelectSQL);
select kafka_ds.*,gimel_load_id
,substr(commit_timestamp,1,4) as yyyy
,substr(commit_timestamp,6,2) as mm
,substr(commit_timestamp,9,2) as dd
,substr(commit_timestamp,12,2) as hh
from pcatalog.KAFKA_dataset kafka_ds
join default.geo_lkp lkp
on kafka_ds.zip = geo_lkp.zip
where geo_lkp.region = ‘MIDWEST’
%%gimel
insert into pcatalog.HIVE_dataset
partition(yyyy,mm,dd,hh,mi)
-- Establish 10 concurrent connections per Topic-Partition
set gimel.kafka.throttle.batch.parallelsPerPartition=10;
-- Fetch at max - 10 M messages from each partition
set gimel.kafka.throttle.batch.maxRecordsPerPartition=10,000,000;

24. Next Steps

25. What’s Next?
• Expand Catalog Provider
• Google Data Catalog
• Cloud Support
• BigQuery
• PubSub
• GCS
• AWS Redshift
• Gimel SQL
• Expand to Cloud Stores
• Query / Access
Optimization
• Pre-empt runaway queries
• Graph Support
• Neo4j
• ML/NLP Support
• ML-Lib
• Spark-NLP

26. Questions?
Code base
http://gimel.io
Gitter
https://gitter.im/paypal/gimel_data_api_community

27. Thank You!

28. Appendix

29. Gimel Thrift Server @ PayPal
29

30. ▪ HiveServer2
service that allows a remote client to submit requests to Hive using a variety of
programming languages (C++, Java, Python) and retrieve results BLOG
▪ Built on Apache Thrift Concepts
▪ Spark Thrift Server
Similar to HiveServer2, executes in spark Engine as compared to Hive (MR
/TEZ)
What is GTS?
• Gimel Thrift Server
Spark Thrift Server
+ Gimel
+ PayPal’s - Unified Data Catalog
+ Security & other PP specific features

31. Depending upon the cluster
capacity and traffic user has to
wait for the session
31
Why GTS?
31
Needs to read data from Hive
through SQL
PayPal
Notebooks
Developer/Analyst/Data
Scientist
2. Starts Spark
Session on
cluster
3. Spark session
Started
1. Get a Spark
Session
4. Submits the query
Select * from
pymtdba.wtransaction_p2
5. Reads from
Store
CLI
Host
APP

32. 32
How does GTS Work?
32
Gimel Thrift
Server
Paypal
Notebooks
Developer/Analyst/Data
Scientist
Needs to read data from Hive
Select * from
pymtdba.wtransaction_p2
1. Submits query to
GSQL Kernal
2. Submits
query to GTS 3. Read from
Store
A
P
P
Connect via Java
JDBC / Python

33. Challenges in platform management
33

34. 34
Challenges | Audit & Monitoring | Multifaceted
DBQLog
s
Audit Table
Cloud Audit Logs
***
Lack of Unified View of Data
Processed on Spark
PubSub
Use
r

35. 35
Platform management Complexities Store Specific Interceptors
PubSub
Store
Operator
s
App
Developer
s
App
s
Instrumentation
By App Developer

36. GTS Key Features
Out-of-box Auditing:
Logging, Monitoring,
Dashboards
Alerting
(beta/internal)
Security
Apache Ranger
Teradata Proxy User
Part of Ecosystem
Notebooks – GSQL
UDC –Datasets
SCAAS – DML/DDL
Low Latency
User Experience
SQL to Any Store
Stores supported by
Gimel
Highly available
architecture
Software & Hardware
Query via REST
(work in progress)
REST
Query Guard
Kills run away
queries