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Not Your Father’s Database:How to Use Apache® Spark™ Properly in Your Big Data Architecture
Not Your Father’s Database:How to Use Apache® Spark™ Properly in Your Big Data Architecture
About Me
2005 Mobile Web & Voice Search
3
About Me
2005 Mobile Web & Voice Search
4
2012 Reporting & Analytics
About Me
2005 Mobile Web & Voice Search
5
2012 Reporting & Analytics
2014 Solutions Engineering
This system talks like a SQL Database…
Is this your Spark infrastructure?
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HDFS
But the performance is very different…
Is this your Spark infrastructure?
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HDFS
Just in Time Data Warehouse w/ Spark
HDFS
Just in Time Data Warehouse w/ Spark
HDFS
Just in Time Data Warehouse w/ Spark
and more…HDFS
Separate Compute vs. Storage
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Benefits:• No need to import your data into Spark to begin
processing.• Dynamically Scale Spark clusters to match compute
vs. storage needs.• Choose the best data storage with different
performance characteristics for your use case.
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Know when to use other data stores besides file systems
Today’s Goal
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Data Warehousing
Use Case:
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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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
Benefits:• Inexpensive Storage• Incredibly flexible processing• Speed and Scale
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Good: General Purpose Processing
Bad: Random Access
sqlContext.sql(“select * from my_large_table where id=2I34823”)
Will this command run in Spark?
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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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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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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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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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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.22
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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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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IncrementalSQL Query
Database Snapshot
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Good: Connecting BI Tools
Tip: Cache your tables for optimal performance.
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HDFS
Bad: External Reporting w/ load
Too many concurrent requests will start to queue up.
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HDFS
Solution: Write out to a DB as a cache to handle load.
Bad: External Reporting w/ load
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HDFS
DB
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Advanced Analytics and Data Science
Use Case:
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.• All in one solution: Data cleansing, featurization,
training, testing, serving, etc.29
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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Streaming and Realtime Analytics
Use Case:
Good: Periodic Scheduled Jobs
Schedule your workloads to run on a regular basis: • Launch a dedicated cluster for important workloads.• Output your results as reports or store to a
files/database.• Poor Man’s Streaming: Spark is fast, so push the
interval to be frequent.
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Bad: Low Latency Stream Processing
Spark Streaming can detect new files dropped into a folder to process, but there is a delay to build up a whole file’s worth of data.
Solution: Send data to message queues not files.
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Thank you
Not Your Father’s Database:How to Use Apache Spark Properly in Your Big Data Architecture
Spark Summit East 2016