Postgres Plus Advanced Server 9.4 gives database administrators greater control and expanded options for customization that boost performance and simplify many common tasks. Among the new features in the release are resource management and compatibility for an expanded set of Oracle functions and applications that boost performance and support developers. The release also features JSONB and other advances in the open source community PostgreSQL for supporting applications with unstructured data, eliminating the need for a standalone NoSQL-only solution.

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Introducing Postgres Plus Advanced Server 9.4
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EnterpriseDB > Resources > Webcasts > Ondemand Webcasts

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• EnterpriseDB and Postgres Plus Overview
• Postgres Plus Advanced Server 9.4
− Resource Management
− Partitioning
− Enhanced SQL (Cube/Rollup/Grouping Sets, Connect_By_Root)
− UTL_HTTP
− SQL/Protect & MTK Enhancements
Agenda

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POSTGRES
innovation
ENTERPRISE
reliability
24/7
support
Services
& training
Enterprise-class
features, tools &
compatibility
Indemnification
Product
road-map
Control
Thousands
of developers
Fast
development
cycles
Low cost
No vendor
lock-in
Advanced
features
Enabling commercial
adoption of Postgres

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The Database
PostgreSQL / Postgres Plus Advanced Server
Tools
xDB Replication
Server
Failover Manager
Management and
Monitoring
Back and
Recovery Tool Postgres Enterprise
Management (PEM)
Stack Builder &
Update Monitor
Cloud Enablement
Cloud Database for
Amazon Web Services Open Stack Support*
Core
OR
PostgreSQL
Postgres Plus
Advanced Server
Extension
Components (PostGIS, pgPool, pgBouncer,
SQL/Protect, PL/Perl Python TCL, FDW)
Connectors (.Net, JDBC, ODBC,OCL)
Utilities (EDBLoader, EDBPlus, ECPGPlus)
Migration Tool Kit
* Roadmap
EnterpriseDB Product Overview
APIs to enable the
Platform as a Service*

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EDB is a Gartner Magic Quadrant Leader

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from PostgreSQL core from EDB Development
• 64 bit LOBs
up to 4TB
in size
• Custom
background
workers
• Writable
Foreign
Data
Wrappers
v9.1
EDB contributions to
PostgreSQL core
• No restore In-place
version upgrades
v9.2
v9.3
v9.0
• Materialized Views
• Deferrable unique
constraints and
Exclusion constraints
• Streaming replication
• Windows
64 bit Support
• Hot standby
• Synchronous
replication
• Serializable
Snapshot Isolation
• In-memory
(unlogged) tables
• Writeable Common
Table Expressions
(WITH)
• Cascaded streaming
replication
• JSON support, Range
Types
• VARRAY support
• SQL Profiler
• Index Advisor
• Parallel Bulk Data
Load
• Row Level Security • Declarative Partitioning
syntax
• Table() function support
for nested tables
• INSERT APPEND hint
• xDB Multi-master
replication
• Expanded Object Type
support
• Partition Read
Improvements
over 75x
• Support for
1000s of
Partitions
• Partition write
improvements
over 400x
• MySQL Foreign Data
Wrappers for SQL/MED
Postgres Plus Advanced Server Key Feature Development
• Index-only scans (covering
indexes)
• Linear read scalability to
64 cores
v9.4
• pg_prewarm
• ALTER SYSTEM
• Concurrently updatable
Materialized Views
• Mongo FDW & MySQL FDW
• Logical
Decoding for
Scalability
• JSONB Data
Type
• JSONB
Indexing
• Expanded
JSON functions
• Delayed
Application of
Replication
• 3x Faster GIN
indexes
• Support for
Linux Huge
Pages
• CPU & I/O
Resource
Management
• SQL Aggregation
with CUBE,
ROLLUP and
GROUPING
SETS
• Comprehensive
UTL_HTTP
Package
• Hash Partitioned
Tables
• Connect_By_Ro
ot Operator for
hierarchical
queries
• SQL/Protect
Logging to DB
Table
• EDB*Loader
Improved Error
handling

7. © 2015 EnterpriseDB Corporation. All rights reserved. 7
CPU & I/O Resource Management
Hash Partitioned Tables
SQL Aggregation with CUBE,
ROLLUP and GROUPING SETS
Comprehensive UTL_HTTP Package
Connect_By_Root Operator
ICU Collation
EDB*Loader Improvements
SQL/Protect Logging to DB Table
Migration Toolkit Enhancements
Postgres Plus Advanced Server Postgres Community
Feature Highlights for Postgres Plus
Advanced Server (PPAS) 9.4
Many new features including:
Logical Change Set Extraction
JSONB Data Type
Time Delayed Standby
ALTER SYSTEM
pg_prewarm()
Materialized View Refresh
Concurrently
Ordered Set Aggregates
and more…
http://www.depesz.com/ is a good reference site
https://commitfest.postgresql.org/ is the global
community site for patches review

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Postgres Plus
Advanced
Server
Resource
Manager
(CPU & I/O)
Reporting
Transactions
80%
20%
Run Mixed Workloads More Efficiently with
PPAS 9.4 Resource Management
• DBA assigns CPU & I/O to job groups
• Allocates and prioritizes consumption of resources
• Low priority jobs don’t hurt high priority jobs

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• Create Resource Groups and assign the CPU Rate Limit
• Use these resource groups during a psql session
Run Mixed Workloads More Efficiently with
PPAS 9.4 Resource Management
- Statements executed will be limited in CPU or writing to shared_buffers per
resource group.
- Individual limits are computed based on recent CPU / shared_buffer usage.
- Processes sleep when necessary & avoid sleep when holding critical locks.
- The limits are adjusted regularly based on current usage.
- If multiple processes in the same group are being executed, aggregate usage
will be limited
CREATE RESOURCE GROUP resgrp_a;
CREATE RESOURCE GROUP resgrp_b;
ALTER RESOURCE GROUP resgrp_a SET cpu_rate_limit = .25;
ALTER RESOURCE GROUP resgrp_a SET dirty_rate_limit = 12288;
SET edb_resource_group TO res_grp_a;

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Chapter 13 of EDB Oracle Compatibility Guide for details on usage
One logically large table is broken into smaller physical pieces.
• Worthwhile when a table would otherwise be very large.
• Exact point to see benefits will depend on the application.
When should I Partition?
• Good rule of thumb is that the size of the table should exceed the physical memory
of the database server.
• When most accessed rows are in a single partition or a small number of partitions.
• When there is a lot of concurrent inserts or updates (Hash partitioning may benefit)
• When a query or update accesses a large percentage of a single partition.
• For Bulk Loads / Unloads (ALTER TABLE faster than bulk load, avoids VACUUM
overhead from DELETE).
• When actively archiving seldom-used data to less expensive storage.
Support Larger Tables with Partitioning

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Use PPAS Syntax To Minimize Partitioning
Errors and Complexity
CREATE TABLE sales (
dept_no number,
part_no varchar2,
country varchar2(20),
date date,
amount number )
PARTITION BY RANGE(date)
(
PARTITION q1_2014 VALUES LESS THAN('2014-
Apr-01'),
PARTITION q2_2014 VALUES LESS THAN('2014-
Jul-01'),
PARTITION q3_2014 VALUES LESS THAN('2014-
Oct-01'),
PARTITION q4_2014 VALUES LESS THAN('2015-
Jan-01')
);
CREATE INDEX sales_date on sales(date);
Simple Declarative
PARTITION BY and
SUBPARTITION BY
Single Index
command
• More SQL syntax provide sophisticated
data management techniques
− ADD / DROP to augment the partitions
− SPLIT to divide the data
− EXCHANGE to swap in a new partition
− TRUNCATE to remove all data but leave
structure in tact
− MOVE to shift data to a different tablespace
• PPAS Improved performance with Fast
Pruning and Constraint Exclusion support
• System Catalog Views for Partitions
− ALL_PART_TABLES
− ALL_TAB_PARTITIONS,
ALL_TAB_SUBPARTITIONS
− ALL_PART_KEY_COLUMNS,
ALL_SUBPART_KEY_COLUMNS

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List, Range or Hash Partition Rules
• Provide the constraints to define where data is stored
• For PPAS, also used to support Fast Partition Pruning
• Consider how data stored will be queried, include often-queried columns
in partitioning rules.
• List – Single partitioning key column; based on exact value
• Range – One of more partitioning key columns; based on values between
two extremes
• Hash (New 9.4) – Data divided amongst equal sized partitions based on
hash value.
* Internal tests have shown hash partitioning can improve performance when many
hundred concurrent connections insert/update to the same table*
PPAS 9.4 Supports Various Partitioning
Rules

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Advanced Server’s Query Planner uses two optimization techniques to
compute an efficient plan:
• Constraint exclusion (constraint_exclusion = partition or on)
− Provided by PSQL
− SELECT w/ WHERE: Query Planner must examine the CHECK constraints defined
for each partition before deciding which partition to send query fragments to.
• Fast pruning (edb_partition_pruning = on)
− Occurs earlier in query planner process. Understands the relationship between
partitions in an Oracle style partitioned table.
− SELECT w/ WHERE: Query Planner can reason that only a certain partition holds
the values without examining the constraints defined for each partition.
− Can be used for LIST or single value RANGE Partitions (not usable on subpartitioned
tables or multi-value range partitioned tables)
− Works with >, >=, =, <=, <, AND, BETWEEN operators in the WHERE Clause
PPAS Improves Partitioning Performance

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• Aggregation Functions (CUBE / ROLLUP / GROUPING SETS)
− From the edb-sample.sql, see dept, emp and jobhist tables to aggregate
employees based on their locations, departments and jobs.
− Chapter 2.2.6 of EDB Database Compatibility Guide for Oracle for more
details on GROUP BY ROLLUP / CUBE / GROUPING SETS and the
subtotals, groupings and result sets generated.
− Your own data for analysis might be involve sales or support related
information. You could report on revenues or tickets grouped by geographic
regions, assigned sales or support engineer, product utilized, etc.
• Connect_By_Root
− Support additional hierarchal query needs. With our edb-sample data, we
can report on various levels of manager employee hierarchies.
− Chapter 2.2.5.6 of EDB Oracle Compatibility Guide for more details on
sample usage
− Your own data for reporting might look at hierarchies of products or modules
Enhanced SQL for Aggregation
and Connect_By_Root Syntax

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• GROUP BY ROLLUP
produces subtotals for each
hierarchal group based on a
left to right listing of items in
the expression
SELECT loc, dname, job, COUNT(*)
AS "employees" FROM emp e,
dept d
WHERE e.deptno = d.deptno
GROUP BY ROLLUP (loc, dname, job)
ORDER BY 1, 2, 3;
Multidimensional Analysis - Rollup

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• GROUP BY CUBE
produces groupings and
subtotals for every
permutation of items in the
expression
SELECT loc, dname, job, COUNT(*)
AS "employees" FROM emp e,
dept d
WHERE e.deptno = d.deptno
GROUP BY CUBE (loc, dname, job)
ORDER BY 1, 2, 3;
Multidimensional Analysis - Cube
……………

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• GROUP BY GROUPING
SETS produces one result
set that is a concatenation
of multiple result sets based
on different groupings (i.e.
UNION ALL)
SELECT loc, dname, job, COUNT(*)
AS "employees" FROM emp e,
dept d
WHERE e.deptno = d.deptno
GROUP BY GROUPING SETS (loc,
dname, job)
ORDER BY 1, 2, 3;
Multidimensional Analysis – Grouping Sets

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• UTL_HTTP – Built in package that provides a way to use the
HTTP(S) protocol to retrieve information found at a URL.
Commonly requested functions now provided:
− REQUEST, REQUEST_PIECES
− GET/SET_FOLLOW_REDIRECT
− GET/SET_RESPONSE_ERROR_CHECK
− GET/SET_HEADER, GET_HEADER_BY_NAME, GET_HEADER_COUNT
− READ_RAW, READ_TEXT
− BEGIN/END_REQUEST, GET_RESPONSE
• Ch 8.17 of EDB Enterprise Edition / Ch 7.17 of EDB Oracle
Compatibility Guide for more details on usage
Develop Applications To Retrieve Data
With UTL_HTTP Package

19. © 2015 EnterpriseDB Corporation. All rights reserved. 19
Preventing attacks is normally the responsibility of
the application developer. But with SQL/Protect,
DBAs can now provide another layer of protection
to prevent corruption or co-opting of the database.
Injection
Attack
Report
SQL/
Protect
Attacker
PREVENTION TECHNIQUES
Unauthorized Relations
Utility Commands (e.g. DDL)
SQL Tautology
Unbounded DML
EDBSQL/PROTECT
DBA Managed with
Centralized SQL
Injection Protection
Threat
Deleted
New in 9.4 - View edb_sql_protect_queries
contains Injection Attack Report information (Ch
4.1.1.2.3 of EDB Enterprise Edition Guide for more
information)

20. © 2015 EnterpriseDB Corporation. All rights reserved. 20
A More Robust Migration Toolkit
New in 9.4
• A detailed log with well defined error codes to
allow DBAs to better understand which
capabilities of their database applications are
migrate-able to PPAS.
• See Ch 9 of Migration Toolkit documentation
for a list of the error codes provided
• Migration of schemas, tables,
constraints, indexes, data,
views and more
• Online (immediate) or offline
(DDL scripts) migration
• Customize migrations with
bulk inserts, row filters,
change datatypes inline,
subsets of schema objects
• Parallel data movement
techniques, bypass logging
for faster data loads or use
native connectivity to source
database.

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1. Customers running mixed workloads.
2. Application Developers who integrate with external Web servers.
3. Customers with large tables where they often search for exact
matches or have many concurrent inserts/updates.
4. Users who think they need a NoSQL database.
5. Customers with reporting or data warehousing databases.
6. DBAs who use need to bulk load data.
7. DBA’s concerned with Security and SQL Injection Attacks.
Recap: Postgres Plus Advanced Server
(PPAS) 9.4 Use Cases

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How can I learn more?
• Download Postgres Plus Advanced Server:
• http://www.enterprisedb.com/download-advanced-server
• General information feature table:
• http://www.enterprisedb.com/postgres-plus-advanced-server
• Set up technical call with a Postgres Expert
• sales@enterprisedb.com

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