This session will cover a series of problems that are adequately solved with Apache Spark, as well as those that are require additional technologies to implement correctly. Here’s an example outline of some of the topics that will be covered in the talk: Problems that are perfectly solved with Apache Spark: 1) Analyzing a large set of data files. 2) Doing ETL of a large amount of data. 3) Applying Machine Learning & Data Science to a large dataset. 4) Connecting BI/Visualization tools to Apache Spark to analyze large datasets internally. By Vida Ha at Spark Summit East 2016.

1. Not Your Father’s Database:
How to Use Apache Spark Properly
in Your Big Data Architecture
Spark Summit East 2016

2. Not Your Father’s Database:
How to Use Apache Spark Properly
in Your Big Data Architecture
Spark Summit East 2016

3. About Me
2005 Mobile Web & Voice Search
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4. About Me
2005 Mobile Web & Voice Search
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2012 Reporting & Analytics

5. About Me
2005 Mobile Web & Voice Search
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2012 Reporting & Analytics
2014 Solutions Engineering

6. This system talks like a SQL Database…
Is this your Spark infrastructure?
6
HDFS
SQL

7. But the performance is very different…
Is this your Spark infrastructure?
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SQL
HDFS

8. Just in Time Data Warehouse w/ Spark
HDFS

9. Just in Time Data Warehouse w/ Spark
HDFS

10. Just in Time Data Warehouse w/ Spark
and more…
HDFS

11. 11
Know when to use other data stores
besides file systems
Today’s Goal

12. Good: General Purpose Processing
Types of Data Sets to Store in File Systems:
• Archival Data
• Unstructured Data
• Social Media and other web datasets
• Backup copies of data stores
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13. Types of workloads
• Batch Workloads
• Ad Hoc Analysis
– Best Practice: Use in memory caching
• Multi-step Pipelines
• Iterative Workloads
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Good: General Purpose Processing

14. Benefits:
• Inexpensive Storage
• Incredibly flexible processing
• Speed and Scale
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Good: General Purpose Processing

15. Bad: Random Access
sqlContext.sql(
“select * from my_large_table where id=2I34823”)
Will this command run in Spark?
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16. Bad: Random Access
sqlContext.sql(
“select * from my_large_table where id=2I34823”)
Will this command run in Spark?
Yes, but it’s not very efficient — Spark may have
to go through all your files to find your row.
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17. Bad: Random Access
Solution: If you frequently randomly access your
data, use a database.
• For traditional SQL databases, create an index
on your key column.
• Key-Value NOSQL stores retrieves the value
of a key efficiently out of the box.
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18. Bad: Frequent Inserts
sqlContext.sql(“insert into TABLE myTable
select fields from my2ndTable”)
Each insert creates a new file:
• Inserts are reasonably fast.
• But querying will be slow…
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19. Bad: Frequent Inserts
Solution:
• Option 1: Use a database to support the inserts.
• Option 2: Routinely compact your Spark SQL table files.
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20. Good: Data Transformation/ETL
Use Spark to splice and dice your data files any way:
File storage is cheap:
Not an “Anti-pattern” to duplicately store your data.
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21. Bad: Frequent/Incremental Updates
Update statements — not supported yet.
Why not?
• Random Access: Locate the row(s) in the files.
• Delete & Insert: Delete the old row and insert a new one.
• Update: File formats aren’t optimized for updating rows.
Solution: Many databases support efficient update operations.
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22. Use Case: Up-to-date, live views of your SQL tables.
Tip: Use ClusterBy for fast joins or Bucketing with 2.0.
Bad: Frequent/Incremental Updates
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Incremental
SQL Query
Database
Snapshot
+

23. Good: Connecting BI Tools
Tip: Cache your tables for optimal performance.
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HDFS

24. Bad: External Reporting w/ load
Too many concurrent requests will overload Spark.
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HDFS

25. Solution: Write out to a DB to handle load.
Bad: External Reporting w/ load
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HDFS
DB

26. Good: Machine Learning & Data Science
Use MLlib, GraphX and Spark packages for machine
learning and data science.
Benefits:
• Built in distributed algorithms.
• In memory capabilities for iterative workloads.
• Data cleansing, featurization, training, testing, etc.
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27. Bad: Searching Content w/ load
sqlContext.sql(“select * from mytable
where name like '%xyz%'”)
Spark will go through each row to find results.
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28. Thank you