1.1
Table Of Contents
- Contents
- About the SQLFire User's Guide
- Supported Configurations and System Requirements
- Getting Started with vFabric SQLFire
- Managing Your Data in vFabric SQLFire
- Designing vFabric SQLFire Databases
- Using Server Groups to Manage Data
- Partitioning Tables
- Replicating Tables
- Estimating Memory Requirements
- Using Disk Stores to Persist Data
- Exporting and Importing Data with vFabric SQLFire
- Using Table Functions to Import Data as a SQLFire Tables
- Developing Applications with SQLFire
- Starting SQLFire Servers with the FabricServer Interface
- Developing Java Clients and Peers
- Configuring SQLFire as a JDBC Datasource
- Storing and Loading JAR Files in SQLFire
- Developing ADO.NET Client Applications
- About the ADO.NET Driver
- ADO.NET Driver Classes
- Installing and Using the ADO.NET driver
- Connecting to SQLFire with the ADO.NET Driver
- Managing Connections
- Executing SQL Commands
- Working with Result Sets
- Storing a Table
- Storing Multiple Tables
- Specifying Command Parameters with SQLFParameter
- Updating Row Data
- Adding Rows to a Table
- Managing SQLFire Transactions
- Performing Batch Updates
- Generic Coding with the SQLFire ADO.NET Driver
- Using SQLFire.NET Designer
- Understanding the Data Consistency Model
- Using Distributed Transactions in Your Applications
- Using Data-Aware Stored Procedures
- Using the Procedure Provider API
- Using the Custom Result Processor API
- Programming User-Defined Types
- Using Result Sets and Cursors
- Caching Data with vFabric SQLFire
- Deploying vFabric SQLFire
- SQLFire Deployment Models
- Steps to Plan and Configure a Deployment
- Configuring Discovery Mechanisms
- Starting and Configuring SQLFire Servers
- Configuring Multi-site (WAN) Deployments
- Configuring Authentication and Authorization
- Configuring User Authentication
- User Names in Authentication and Authorization
- Configuring User Authorization
- Configuring Network Encryption and Authentication with SSL/TLS
- Managing and Monitoring vFabric SQLFire
- Configuring and Using SQLFire Log Files
- Querying SQLFire System Tables and Indexes
- Evaluating Query Plans and Query Statistics
- Overriding Optimizer Choices
- Evaluating System and Application Performance
- Using Java Management Extensions (JMX)
- Best Practices for Tuning Performance
- Detecting and Handling Network Segmentation ("Split Brain")
- vFabric SQLFire Reference
- Configuration Properties
- JDBC API
- Mapping java.sql.Types to SQL Types
- java.sql.BatchUpdateException Class
- java.sql.Connection Interface
- java.sql.DatabaseMetaData Interface
- java.sql.Driver Interface
- java.sql.DriverManager.getConnection Method
- java.sql.PreparedStatement Interface
- java.sql.ResultSet Interface
- java.sql.SavePoint Class
- java.sql.SQLException Class
- java.sql.Statement Class
- javax.sql.XADataSource
- sqlf Launcher Commands
- sqlf backup
- sqlf compact-all-disk-stores
- sqlf compact-disk-store
- sqlf encrypt-password
- sqlf install-jar
- sqlf list-missing-disk-stores
- sqlf locator
- sqlf Logging Support
- sqlf merge-logs
- sqlf remove-jar
- sqlf replace-jar
- sqlf revoke-missing-disk-store
- sqlf server
- sqlf show-disk-store-metadata
- sqlf shut-down-all
- sqlf stats
- sqlf upgrade-disk-store
- sqlf validate-disk-store
- sqlf version
- sqlf write-data-dtd-to-file
- sqlf write-data-to-db
- sqlf write-data-to-xml
- sqlf write-schema-to-db
- sqlf write-schema-to-sql
- sqlf write-schema-to-xml
- sqlf Interactive Commands
- absolute
- after last
- async
- autocommit
- before first
- close
- commit
- connect
- connect client
- connect peer
- describe
- disconnect
- driver
- elapsedtime
- execute
- exit
- first
- get scroll insensitive cursor
- GetCurrentRowNumber
- help
- last
- LocalizedDisplay
- MaximumDisplayWidth
- next
- prepare
- previous
- protocol
- relative
- remove
- rollback
- run
- set connection
- show
- wait for
- SQLFire API
- SQL Language Reference
- Keywords and Identifiers
- SQL Statements
- ALTER TABLE
- CALL
- CREATE Statements
- DECLARE GLOBAL TEMPORARY TABLE
- DELETE
- EXPLAIN
- DROP statements
- GRANT
- INSERT
- REVOKE
- SELECT
- SET ISOLATION
- SET SCHEMA
- TRUNCATE TABLE
- UPDATE
- SQL Queries
- SQL Clauses
- SQL Expressions
- JOIN Operations
- Built-in Functions
- Standard Built-in Functions
- Aggregates (set functions)
- ABS or ABSVAL function
- ACOS function
- ASIN function
- ATAN function
- ATAN2 function
- AVG function
- BIGINT function
- CASE expressions
- CAST function
- CEIL or CEILING function
- CHAR function
- COALESCE function
- Concatenation operator
- COS function
- COSH function
- COT function
- COUNT function
- COUNT(*) function
- CURRENT DATE function
- CURRENT_DATE function
- CURRENT ISOLATION function
- CURRENT_ROLE function
- CURRENT SCHEMA function
- CURRENT TIME function
- CURRENT_TIME function
- CURRENT TIMESTAMP function
- CURRENT_TIMESTAMP function
- CURRENT_USER function
- DATE function
- DAY function
- DEGREES function
- DOUBLE function
- EXP function
- FLOOR function
- HOUR function
- INTEGER function
- LCASE or LOWER function
- LENGTH function
- LN or LOG function
- LOG10 function
- LOCATE function
- LTRIM function
- MAX function
- MIN function
- MINUTE function
- MOD function
- MONTH function
- NULLIF expressions
- PI function
- RADIANS function
- RANDOM function
- RAND function
- RTRIM function
- SECOND function
- SESSION_USER function
- SIGN function
- SIN function
- SINH function
- SMALLINT function
- SQRT function
- SUBSTR function
- SUM function
- TAN function
- TANH function
- TIME function
- TIMESTAMP function
- TRIM function
- UCASE or UPPER function
- USER function
- VARCHAR function
- XMLEXISTS operator
- XMLPARSE operator
- XMLQUERY operator
- XMLSERIALIZE operator
- YEAR function
- SQLFire Built-in Functions
- Standard Built-in Functions
- Built-in System Procedures
- Standard Built-in Procedures
- SYSCS_UTIL.EMPTY_STATEMENT_CACHE
- SYSCS_UTIL.EXPORT_QUERY
- SYSCS_UTIL.EXPORT_TABLE
- SYSCS_UTIL.IMPORT_DATA
- SYSCS_UTIL.IMPORT_DATA_EX
- SYSCS_UTIL.IMPORT_DATA_LOBS_FROM_EXTFILE system procedure
- SYSCS_UTIL.IMPORT_TABLE
- SYSCS_UTIL.IMPORT_TABLE_EX
- SYSCS_UTIL.IMPORT_TABLE_LOBS_FROM_EXTFILE
- SYSCS_UTIL.SET_EXPLAIN_CONNECTION
- SYSCS_UTIL.SET_STATISTICS_TIMING
- JAR Installation Procedures
- Callback Configuration Procedures
- Heap Eviction Configuration Procedures
- WAN Configuration Procedures
- Standard Built-in Procedures
- Data Types
- SQL Standards Conformance
- System Tables
- ASYNCEVENTLISTENERS
- GATEWAYRECEIVERS
- GATEWAYSENDERS
- INDEXES
- JARS
- MEMBERS
- MEMORYANALYTICS
- STATEMENTPLANS
- SYSALIASES
- SYSCHECKS
- SYSCOLPERMS
- SYSCOLUMNS
- SYSCONGLOMERATES
- SYSCONSTRAINTS
- SYSDEPENDS
- SYSDISKSTORES
- SYSFILES
- SYSFOREIGNKEYS
- SYSKEYS
- SYSROLES
- SYSROUTINEPERMS
- SYSSCHEMAS
- SYSSTATEMENTS
- SYSSTATISTICS
- SYSTABLEPERMS
- SYSTABLES
- SYSTRIGGERS
- SYSVIEWS
- Exception Messages and SQL States
- ADO.NET Driver Reference
- SQLFire Data Types in ADO.NET
- VMware.Data.SQLFire.BatchUpdateException
- VMWare.Data.SQLFire.SQLFClientConnection
- VMware.Data.SQLFire.SQLFCommand
- VMware.Data.SQLFire.SQLFCommandBuilder
- VMware.Data.SQLFire.SQLFType
- VMware.Data.SQLFire.SQLFDataAdapter
- VMware.Data.SQLFire.SQLFDataReader
- VMware.Data.SQLFire.SQLFException
- VMware.Data.SQLFire.SQLFParameter
- VMware.Data.SQLFire.SQLFParameterCollection
- VMware.Data.SQLFire.SQLFTransaction
- vFabric SQLFire Limitations
- Troubleshooting Common Problems
- vFabric SQLFire Glossary
- Index
Embedded Peer-to-Peer Deployment
The embedded, peer-to-peer distributed system (also known as an embedded cluster) is the building block for
all vFabric SQLFire installations. With an embedded cluster, the core SQLFire engine is embedded alongside
an existing Java application in the same JVM. When the application initiates a connection to SQLFire using the
peer JDBC driver, it starts a SQLFire peer member that joins other peers in the same cluster.
• Understanding Embedded Peer-to-Peer Deployment on page 210
• Deciding When to Use Embedded Peer-to-Peer on page 210
• Example Code for Embedding SQLFire Members on page 211
Understanding Embedded Peer-to-Peer Deployment
The embedded peer-to-peer model provides embedded Java applications with default one-hop or no-hop access
to data. Each peer member communicates directly with the other members of the cluster and can host data as
necessary, persist data to disk (if it is a data store), and participate in distributed queries. All peers are treated as
"fully authorized" by the SQLFire security framework after they are authenticated.
An embedded SQLFire peer can optionally provide network server functionality for applications that need to
support connectivity from JDBC or ADO.NET clients. Or, you can embed SQLFire in a cluster of application
servers such as Apache Tomcat or Oracle WebLogic, which provide their own network server functionality. A
single attribute in the JDBC peer driver connection determines whether the SQLFire peer also provides network
services.
Embedding SQLFire peer services is supported only for Java applications. Existing Java applications can easily
switch to using an embedded SQLFire peer by including the required SQLFire libraries and specifying the
SQLFire JDBC peer client URL.
Note: In this release of SQLFire, ADO.NET clients cannot connect as peers.
Deciding When to Use Embedded Peer-to-Peer
Here are typical scenarios in which you would use the embedded peer-to-peer model:
• Many applications frequently accessing content directly from within the process heap (for example,
session state data or other Web content). The embedded peer-to-peer model automatically provides one-hop
or no-hop access to data. The SQLFire JDBC thin-client driver also supports one-hop access for lightweight
client applications.
• More convenient from an administration and management standpoint. With an embedded cluster, there
is no need to manage any external processes in order to deploy SQLFire peers. For example, if you embed
SQLFire in a cluster of Java application servers, each application server and the associated SQLFire peer share
the same process heap.
• Applications requiring a few peer clients (as opposed to hundreds). Deploying many peers increases the
buffering and socket overhead for each member. Having numerous peers can also strain the group membership
system coordinator, and it increases the overhead necessary for detecting failures. In an embedded cluster
deployment, all SQLFire transactions and distributed locks are maintained by the peers themselves. This is in
contrast to the client-server deployment model, in which clients are required to execute server-side code (such
as stored procedures) in order to acquire locks or execute transactions.
• Partitioned caching applications in which a small number of "feed" clients push data to an RDBMS at
a very fast rate.This scenario is particularly suited to embedded peer-to-peer. SQLFire peer members dispatch
messages to other peers synchronously by default, and with minimal context-switching. This provides the
lowest possible latency and offers the highest distribution throughput for both replicated caching as well as
partitioned caching applications. It also offers the lowest distribution latency, which useful for latency-sensitive
"feed" clients.
vFabric SQLFire User's Guide210
Deploying vFabric SQLFire