Orchestrating Messaging, Data Grid and Database

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Orchestrating Messaging, Data Grid and Database

• Jon Purdy
• Oracle Corporation

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Notes

• Companies and Products
• Oracle acquired Tangosol back in June
• Coherence is a Data Grid solution
• Questions are encouraged

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Agenda

• Technology Stack Overview
• Introduction to Data Grid technology
• Application State
• Types of State
• Challenges
• Putting it together
• How state is managed by application tiers
• How to integrate application tiers
• How Data Grids can fill in the gaps

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Technology Stack Overview

• There are many tools for building scalable,
  reliable systems
• Messaging
• Application Servers
• Data Grids
• Databases
• What types of state do these manage?
• When should each one be used?

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Technologies

• Messaging
• Integration between systems (queues)
• Distributing relevant data (topics)
• Application Servers
• Request processing
• Conversational state
• Data Grids
• Scalability and performance
• Conversational state and/or limited persistent
  state
• Databases
• Persistent state
• Reliable, shared conversational state (if needed)

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Technologies
Messaging
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Data Grids What are they?

• Special-purpose data management solution
• Live, transactional data at in-memory speed
• First class programmatic access
• Built from the ground-up for in-memory efficiency
• Avoids CPU overhead of disk management
• Usually a native object view of data
• Less flexible than a true database
• Query optimization is an unsolvable problem
• Three decades of RDBMS evolution offsets that
• Less focus on long-term storage

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Data Grids

• Extend the coherency protocol to client
  applications
• Take advantage of the native object view of
  data
• Keep important data local for efficiency
• OR/M can sometimes be slower than the actual
  query
• Implementations
• Oracle Coherence
• GemStone GemFire
• IBM ObjectGrid

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A Brief History
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Relational DBMS

• Relational DBMS
• Relational structure allows any view of data
• Minimizes impact of data schema mistakes
• Databases for The People
• With 4GL tools, led to the Client-Server
  revolution
• And even power users Microsoft Excel and Access
• The critical ingredient Query Optimizer
• DBMS assumes responsibility for optimizing data
  access

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Relational DBMS

• But
• Static optimization (RBO) is not 100 reliable
• Dynamic optimization (CBO) is not 100 reliable
• Mistakes magnified with scale and load
• Scalability and availability problems

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Object DBMS

• Brief appearance in late 80s / early 90s
• Some impressive performance feats
• Extremely efficient for intended access patterns
• Data schema coupled to business logic
• Difficult to evolve data schema
• Market segment as a whole has died
• A few stragglers left

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The best of all worlds

• Take the efficiency of an Object DBMS
• In-memory data coupled to application access
  patterns
• Consistent access patterns at runtime
• Add scale-out as a primary objective
• And leverage the RDBMS
• Existing storage resources and skills
• Loosely coupled data schemas

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How does it work?
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Partitioned Cache
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Partitioned Cache
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Partitioned Cache
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Near Cache
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• Types of State
• Characteristics

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Types of State

• Messages
• Request/Response
• Source user, message queue or another
  application tier
• Show inventory list (display web page in
  browser)
• Just a message from one system to another
• Conversational State
• Stateful Applications
• Spans multiple requests (a conversation)
• Add item to shopping cart (update HTTP session)
• Internal state
• Persistent State
• Typically stored in a database
• Place order (persist order to database)
• Externally visible

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Connecting the dots

• Applications process requests, taking into
  account the context of those requests, to manage
  persistent data
• Therefore, effective applications must ensure
  that
• Requests are properly processed
• Proper context is maintained
• Persisted data is correct
• All of this is done in a timely manner

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Characteristics Messages

• Short-lived
• Interactive apps milliseconds to a few seconds
• Integration similar, unless one of the systems
  is down
• Immutable and single-writer pattern
• By definition, each request submitted by a single
  system
• Almost no way to corrupt state, and easy to avoid
  losing state
• Stateless applications are very easy to scale
• Simple request-response processing
• Requests are often retry-able (idempotent)

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Characteristics Conversational State

• Longer-lived
• A few seconds to several minutes
• Mutable, but by a single user
• Not quite single-writer
• Simultaneous requests from a user
• Multiple portlets in a portal application
• Multiple clicks at the same time
• Load-balancing issues failover/failback/rebalanci
  ng
• Often recoverable
• Worst case, by restarting the session

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Characteristics Persistent State

• Long-lived
• Rarely less than a few days often many decades
• Often have regulatory requirements for several
  years
• Mutable and globally shared
• Possible interaction and contention from all
  users
• Concurrency and data consistency are hard to
  combine
• The entire application shares one persistent state

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Summary Managing State
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• Types of State
• Challenges

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Challenges

• Messages
• Most considerations relate to interactions
  between systems
• These interactions are effectively distributed
  transactions
• It is critical to manage these transactions
  both reliably and efficiently

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Challenges

• Conversational state
• Most applications can tolerate modest corruption
  (or loss) of conversational state (or do anyway)
• Those that cant assume this will generally place
  this state in a reliable data store, or avoid
  conversational state altogether
• While technology solutions exist, scaling
  stateful applications remains a challenge

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Challenges

• Persistent state
• As the System of Record, persistent state is
  the most valuable asset
• Databases are the default option for properly
  managing persistent state
• However, scaling and performance concerns often
  move data management out of the database,
  increasing the difficulty of managing it correctly

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Impact of lost/corrupted data

• Messages
• User gets a failed request
• User resubmits request (click again)
• Impact limited in scope (one user) and time (one
  request)
• Conversational State
• Users session is corrupted or missing
• If detected by the system, user may need to log
  in again and start over
• If not detected, the user will usually (but not
  always) notice
• Impact limited in scope (one user) and time (one
  session)
• Persistent State
• Persistent State is the primary objective!
• For the user Payment received but order not
  shipped
• For everyone Inventory levels are incorrect
• Impact is global for all users and for all time!

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Critical Areas of Concern

• Messages
• Conversational State
• Persistent State

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• Messaging
• Compare, Contrast, Integrate

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Messaging

• Topics
• One-to-many subscribers sign up to topics of
  interest
• All subscribers receive messages as they occur
• Emphasis on fast delivery to many subscribers
  (performance, scalability)
• Queues
• Used primarily for communication between two
  systems
• Physical decoupling of sender and receiver
• Emphasis on reliable message delivery
  (durability)
• Implementations
• TIBCO Rendezvous, IBM MQSeries

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Messages

• Requests typically flow through multiple systems
• Message Queue ? App Server ? Database
• Browser ? Web Server ? App Server ? Database
• Ensure that each request is processed
• even if a participating service fails
• Failure of either client or server can result in
  dropped or duplicated requests
• Most common requirement is once and only once
  but other variants may be acceptable (at most
  once, at least once)

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Traditional Message Processing

• Integrating multiple systems may require
  distributed transactions (XA)
• Distributed transactions
• Simple to integrate minimal effect on
  application architecture
• E.g. enlist both the database and the queue
• Slow (disk forces)
• Tendency to cause lock contention (two-phase
  locking)
• Not 100 reliable (heuristic failures)
• Not widely supported (lack of support,
  compatibility issues)

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Idempotency

• Concept
• If the client knows the server can handle
  duplicate requests
• Then the client can err on the side of re-sending
  in doubt requests
• A partial failure results in a complete retry
• No need to use XA to coordinate client and server
• Impact
• May have a noticeable impact on application
  architecture
• Fast
• Very reliable

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Message Processing with XA

• JMS begin TX
• DB begin TX
• Read message
• Write to database
• Prepare JMS
• Prepare DB
• Commit JMS
• Commit DB
• If the prepare phase fails in either JMS or DB,
  the DB transaction is rolled back, and the JMS
  message is left in the queue
• If the commit phase fails, that is a heuristic
  failure the state of the transaction is unknown

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Idempotent Message Processing with Local
Transactions

• JMS begin TX
• DB begin TX
• Read message
• Write to DB (Idempotent)
• Commit DB
• Commit JMS
• If commit to DB fails, the entire operation is
  aborted the message is still in the queue
• If commit to JMS fails, the JMS de-queue is
  rolled back (but the DB commit isnt)
• The next time the message is processed, the write
  to the DB will occur, but the operation wont
  have undesired side effects

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Data Grid and Messaging

• Data Grids can be used as a messaging fabric
• But introduces global visibility of a new
  infrastructure piece
• Established players have more mature solutions
• And operations team know these products
• Messaging usually used within the Data Grid
• Not between disparate applications
• One exception
• Data Grids can use write-behind queueing to avoid
  the need for a dedicated message broker
• Queue the messages in memory, not on disk
• Slight reduction in durability but reduces
  operating costs

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• Application Server
• Compare, Contrast, Integrate

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Application Servers

• Application containers
• Provide a framework for managing requests and
  (usually) conversational state
• May manage lifecycle of application deployment
  packages
• Also service directories (JNDI / Jini lookup
  services)
• Implementations
• JavaEE WebLogic, WebSphere, JBoss, Oracle AS,
  etc.
• Compute Grid Platform Symphony, DataSynapse
  GridServer
• Jini Blitz, GigaSpaces
• Spring
• Requests
• Route incoming requests (e.g. from TCP socket) to
  application components
• Conversational State
• JavaEE HTTP sessions (conversation between user
  and web server)
• Jini JavaSpaces (conversation between multiple
  processes)

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Conversational State Topologies

• In-memory (no replication)
• Fastest, most scalable option
• Server failure results in data loss
• Single-server visibility (dependent on sticky
  load balancer)
• In-memory (replication)
• Fast, scalable (implementations vary)
• Widely available, sufficient for most use cases
• Most implementations are not fully coherent under
  load or failure
• Database persistence
• Higher complexity and lower performance
• Achieves data consistency, commonly available
• Scales with database server (for better or worse)

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Conversational State

• Unreliable conversational state
• No in-memory replication (data loss)
• Incoherent in-memory replication (data
  corruption)
• Tools
• Idempotent processing
• Reliable data store
• Concept
• Use application and data store to verify
  correctness on commit
• Verify order placement on web page
• Use optimistic concurrency on database to check
  values
• Use idempotent processing to retry request chain
• Buyer corrects shopping cart and resumes checkout
  process
• Or for closed-loop systems, recover missing
  conversational state by replaying requests or
  re-loading from database (selectively persisted
  for performance)

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• Database
• Compare, Contrast, Integrate

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Database

• The only real solution for persistence?
• Permanent System of Record
• Guaranteed data consistency
• Operations
• Perhaps the most widely deployed technology
• In-house operations teams already know how to use
• Strongest query technology (robust cost-based
  optimizers)
• Plenty of support 3rd party tool vendors,
  consultants, documentation, discussion forums,
  etc.

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Database

• Usually the easiest and most reliable solution
  for managing persistent state
• But supply
• Absolute requirement for data consistency
• Consistency requirements make scaling difficult
  (but possible)
• may not meet demand
• Front tiers are inexpensive and easy to scale
• Scaling on the front causes massive load on the
  back
• Offloading can help with managing persistent data
• Eventually faces diminishing returns from
  overhead and complexity

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Offloading via Caching

• Keep a local partial data set for faster access
• Beneficial for read-heavy applications
• Gained popularity by mitigating the EJB BMP N1
  problem
• Limited gains for transactions and queries
• Relatively transparent to application
  architecture
• Weak requirements for data consistency
• With optimistic concurrency, data consistency is
  delegated to SoR
• For presentation layer, dirty reads are often
  acceptable

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Offloading Analytics

• Run queries against a copy of the System of
  Record
• System of Reference
• Data consistency is important
• Depends on usage
• Generally operating against a point-in-time
  snapshot
• Data resilience is a Quality of Service
  consideration
• Recoverable from the System of Record
• Failure will affect availability but not results

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Offloading Events

• Changes to the System of Record may need to
  trigger additional processing
• Challenges
• Ensuring all changes of any relevant state are
  handled in a timely manner
• Absolute data consistency required for change
  events and the context of those events (ordering,
  subscribers, etc)
• Hard to do all of these
• Absolute data consistency
• Fan-out of events from transactions
• Timely delivery of events

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Offloading Transactions

• The System of Record must manage all transactions
  related to its owned data
• But a given piece of data may have different
  owners over even short periods of time
• Important to identify which system owns each
  piece of data
• Usually achieved by owning part of the
  permanent store
• Data consistency required

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Data Grids can help

• Conversational state
• Combine the data consistency of a database with
  the performance of local in-memory data
• Persistent state
• Running queries in the data grid can remove the
  query load on a database
• Committing transactions in-memory then persisting
  in batches can reduce the transaction load of a
  database
• Abstraction of data sources

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Data Source Integration - Read Through
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Data Source Integration -Write Through
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Data Source Integration -Write Behind
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Data Grid Data Source Integration

• Data Integration occurs in the Data Service
• Integration uses the domain model
• The data is both live and shared
• Events provide bi-directional flow
• Applications can respond to events

Data Service Clients
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Summary of Data Grid Integration Points

• Messaging
• Data Grid can be used for internal application
  messaging
• Application Server
• Scale data availability reliably along with
  processing power
• Database
• Offload transactions and analytics to Data Grid
  for higher throughput

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The Spectrum
Integration
Messaging Topics, Queues
Data Consistency
Application Servers Requests JavaEE, Jini,
Compute Grid Conversational HTTP Sessions,
Stateful EJBs, JavaSpaces
Scalable Performance
Data Grids Data Grid, In-Memory Database
Database
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• Thank You!