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Cloud AnalyticsData Warehousing

Marco Serafini

COMPSCI 590SLecture 19

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Trivia• How does Amazon make money?

• Selling books?• Entertainment?

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Cloud Computing• Shared resources

• Multiple tenants sharing resources (with isolation)• Economy of scale

• Elastic provisioning• Can easily add and remove resources on the fly

• Pay as you go only when used• Different flavors

• IaaS, PaaS, SaaS• Public, private cloud

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Cloud Offerings• Computing nodes

• Example: AWS EC2• Full nodes with local storage and pre-installed OS• Very large number of instance types: compute optimized, memory optimized, storage optimized, with GPUs, burstable…

• Storage services• Example: AWS S3• Key-value stores (put/get), file systems

• Higher-level services• Example: DBMS

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Other Variants• Spot instances

• Allocated in real-time based on live bidding• Can be revoked any time (with notice)

• Serverless computing• Example: AWS Lambda

• Each of these services comes with own pricing

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Storage Disaggregation• Use remote storage instead of local storage

• Network is fast• Remote and local storage can have same throughput

• Advantages: can use cloud storage services like S3• No configuration or provisioning needed• Cheaper

• Cost of disaggregated storage• Storage nodes can have weak CPUs and limited memory• Storage is cheap

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Remote vs. Local Storage

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Goals• Easily parallelize single-threaded code• Eliminate cluster management overhead

• Deployment of nodes• Installation• Configuration

• Even cloud offerings have their complexities• Many instance types• Many services

• Solution: Serverless functions

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Serverless Functions• Single threaded code• Invoked through HTTP requests• Cloud platform takes care of

• Deployment• Load balancing• Performance isolation

• No need to• Deploy servers• Configure clusters

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State and Fault Tolerance• State is lost after execution• Inputs and outputs need to be persisted• Fault tolerance

• Re-execute function• Require atomic writes to check what has succeeded

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Registering Functions• Registering a new Lambda function is slow• Solution

• Register a single generic Lambda function• Serialize the code that needs the be executed• Store the code (and the input data) on S3• Generic Lambda function loads code and executes it

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Remote Storage Scalability

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Semantics• Map is easy

• Execute one function per element of the list• Map + single Reducer

• E.g. parallel featurization + single-server ML• MapReduce

• Many Lambdas needed, many small intermediate files• Use Redis, an in-memory key-value store

• Parameter server• Use Redis

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The Cost of Scaling Up• Using more nodes does not always imply higher cost• Lower latency à lower cost per node

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Shared-Nothing and the Cloud• Shared-nothing architecture

• Each node has its own disk and memory• All nodes are “symmetric”

• Challenges• Heterogeneous workloads

• No one-size-fits-all hardware configuration• Membership changes

• Large data shuffles when a node fails/is removed• Online upgrade

• It is similar to changing all the nodes in the system

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Architecture• Data Storage

• Based on S3: high throughput, high latency• Used also for intermediate data

• Virtual Warehouses• Responsible for query execution• Stateless (restarted in their entirety)• Shared cache (low latency on hot data, most data cold)

• Cloud Services• Query parsing, access control, optimization• Snapshot isolation with multi-versioning• Metadata on external key-value store

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Advantages• Storage on S3 is cheaper• Use expensive local disk only for hot data• All services (except storage) are stateless

• Simpler fault tolerance and membership change• Example: online upgrade

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SparkSQL: Spark + DBMS• Extend Spark with

• Simple, high-level SQL-like operators• Query optimization

• No need to transfer data across systems• ETL, query processing, complex analytics in one system

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DataFrames• Collection of rows with homogeneous schema

• Like a table in a DBMS• Can be manipulated like an RDD

• DataFrame operations• Similar to Python Pandas or R data frames• Evaluated lazily (query planning is postponed)• Can optimize across multiple queries

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Advantages• Relational structure enables query optimization• In-memory caching using columnar representation

• Better compression• Mix SQL-like operators and arbitrary code

• More flexible than UDFs in DBMSs• Can optimize across multiple SQL operations

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Catalyst• Query optimizer of SparkSQL• Rule-based optimization

• Rule: find pattern and transform• Used for both logical and physical plans• Can customize rules

• Code generation• Directly outputs bytecode (as opposed to interpreting a plan)• Much more CPU efficient

• Flexible data sources• Can change the physical representation of DataFrames• Still use the optimizer