IBM Informix xC2 Enhancements IBM Corporation

Transcription

1 IBM Informix xC2 Enhancements 2013 IBM Corporation

2 Acknowledgements and Disclaimers Availability. References in this presentation to IBM products, programs, or services do not imply that they will be available in all countries in which IBM operates. The workshops, sessions and materials have been prepared by IBM or the session speakers and reflect their own views. They are provided for informational purposes only, and are neither intended to, nor shall have the effect of being, legal or other guidance or advice to any participant. While efforts were made to verify the completeness and accuracy of the information contained in this presentation, it is provided AS-IS without warranty of any kind, express or implied. IBM shall not be responsible for any damages arising out of the use of, or otherwise related to, this presentation or any other materials. Nothing contained in this presentation is intended to, nor shall have the effect of, creating any warranties or representations from IBM or its suppliers or licensors, or altering the terms and conditions of the applicable license agreement governing the use of IBM software. All customer examples described are presented as illustrations of how those customers have used IBM products and the results they may have achieved. Actual environmental costs and performance characteristics may vary by customer. Nothing contained in these materials is intended to, nor shall have the effect of, stating or implying that any activities undertaken by you will result in any specific sales, revenue growth or other results. Copyright IBM Corporation All rights reserved. U.S. Government Users Restricted Rights - Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM Corp. IBM, the IBM logo, ibm.com, Informix, are trademarks or registered trademarks of International Business Machines Corporation in the United States, other countries, or both. If these and other IBM trademarked terms are marked on their first occurrence in this information with a trademark symbol ( or ), these symbols indicate U.S. registered or common law trademarks owned by IBM at the time this information was published. Such trademarks may also be registered or common law trademarks in other countries. A current list of IBM trademarks is available on the Web at Copyright and trademark information at Other company, product, or service names may be trademarks or service marks of others.

3 Agenda About Informix xC2 New Features and Other Enhancement Areas NoSQL Support In-Memory Analytics / Warehousing / Informix Warehouse Accelerator Generic Performance Improvement Sensor Data (Time Series) Management IBM Corporation

4 About Informix xC2 Informix database server xC2 Release Dates: ega September 13 th, 2013 GA October 4 th, 2013 Informix products released along with the database server: Informix database server xC2 Client Software Development Kit (SDK) /Connect 4.10.xC2 JDBC 4.10.JC2 OpenAdmin Tool (OAT) for Informix 3.12

5 Fixpack xC2 13th September 2013 Installation Installation enhancements Migration Server changes Converting xC1 databases for JSON compatibility Reversion requirements Administration Error handling New event alarm for network failures IBM OpenAdmin Tool (OAT) for Informix Customized help is not available Find what you need by searching OAT Back up and restore Enhanced support for IBM Tivoli Storage Manager features Restore critical files ON-Bar activity log timestamps Configuration Compare onconfig files Performance Faster operations In-place alter operations on serial data types Faster queries for the Committed Read Last Committed isolation level Dynamic tuning Dynamic private memory caches for CPU virtual processors Troubleshooting Monitor resource contention Security Transport Layer Security (TLS) versions 1.0, 1.1 and 1.2 enabled by default Application development JSON compatibility JSON compatibility JSON compatibility in OAT New syntax for queries Joins with lateral references Basic text searching Enhanced basic text searching Client products Informix.NET Provider support for.net Framework versions New default Defining separators for fractional seconds in date-time values Enterprise Replication Easier setup Simplified schema changes for replicated tables Set up and query time series data through a grid Configuration Control the replication of large objects Custom checksum function for consistency checking Sharding Shard tables across database servers Time series data Enhancements Replicate time series data with all high-availability clusters Order TimeSeries columns in query results Improvements for time series loader programs Faster queries Faster aggregation of an interval of time series data Faster queries on time series virtual tables Accelerate queries on time series data Warehousing Additional types of data Accelerate queries on time series data Load data from external tables into data marts

6 New NoSQL Functionality Added three new built-in data-types LongLvarchar JSON BSON New data types are native to all databases Automatically convert JSON to BSON document Automatically converts BSON to JSON Added new Built-in BSON Functions Support for MongoDB Client APIs To access both JSON/BSON documents and Relational Tables Sharded Operations to enable Horizontal Scaling Insert, Delete, Update Added Simplification - Autonomics Installation Resource Allocation 6

7 JSON: JavaScript Object Notation What is JSON? JSON is lightweight text-data interchange format JSON is language independent JSON is "self-describing" and easy to understand JSON is syntax for storing and exchanging text information much like XML. However, JSON is smaller than XML, and faster and easier to parse. { "name":"john Miller", "age":21, "count":27, "employees": [ { "firstname":"john", "lastname":"doe" }, { "firstname":"anna", Middle : Marie,"lastName":"Smith" }, { "firstname":"peter", "lastname":"jones" } ] } 7

8 JSON Details JSON Syntax Rules JSON syntax is a subset of the JavaScript object notation syntax: Data is in name/value pairs Data is separated by commas Curly braces hold objects Square brackets hold arrays JSON Name/Value Pairs JSON data is written as name/value pairs. A name/value pair consists of a field name (in double quotes), followed by a colon, followed by a value: JSON Values can be A number (integer or floating point) A string (in double quotes) A Boolean (true or false) An array (in square brackets) An object (in curly brackets) Null "name":"john Miller" 8

9 Understanding Informix BSON Indexes BSON is binary form of JSON Indexes are created on BSON data, and support.. Arrays Composite Indexes Unique Indexes (enforced at a single node level) Primary Key (enforced across all nodes) { "fname":"sadler", "lname":"sadler", "company":"friends LLC", "age":21, "count":27, phone": [ , ], } create index fnameix1 on customer(bson_value(bson,"fname")) using bson; create index lnameix2 on customer(bson_value(bson,"lname")) using bson; create index phoneix3 on customer(bson_value(bson,"phone")) using bson; 9

10 Understanding Informix BSON Indexes create index fnameix1 on customer(bson_value(bson,"fname")) using bson; create index lnameix2 on customer(bson_value(bson,"lname")) using bson; create index phoneix3 on customer(bson_value(bson,"phone")) using bson; select * from customer where bson_value(bson,"fname") = "Ludwig"; -- use fnameix1 select * from customer where bson_value(bson,"lname") = "Sadler"; -- use lnameix2 select * from customer where bson_value(bson,"phone") = " "; -- use phoneix3 select * from customer where bson_value(bson,"phone") = " " OR bson_value(bson,"phone") = " "; -- use phoneix3 select * from customer where bson_value(bson,"company") = "Los Altos Sports"; -- no index use sequential scan 10

11 Support for MongoDB Client APIs Compatible with all MongoDB programming interfaces Connect the same application developed for MongoDB to Informix with minimal/no application changes Access both JSON/BSON documents and Informix Relational Tables Utilize the same components to access rows stored in tables MongoDB API Applications JSON Operations JSON Data PHP JavaScrip t MongoDB Wire Protocol JSON Listener JSON Queries <> SQL JDBC Java Python And Other s SQL Command Pass Through Enable SQL commands execution within MongoDB operations Informix JSON/BSON Storage Architecture of the Informix NoSQL components IBM Corporation

12 Sharded Operations Sharded operations added to sharded query capability Provided Sharded Insert, Update, Delete and Query operations Application A Listener 1 N 1 Collections Foo shard 1 Bar 2 Super scale out Simplify the ability to scale out to multiple nodes, multiple versions, multiple copies Provided diskless and disk based scale out at the individual node with automatic failover Cloud and Virtualized environment supportability Foo shard N 3 Foo shard 2 Foo shard 3 Additional Autonomics Prototype of Informix shard cluster. Further Simplified the industry leading up and running experience with added autonomics Automatically tune the data store with only 3 questions: Where to place the Product? Where to place the Data? How many users do you anticipate? IBM Corporation

13 Informix Warehouse Accelerator (IWA) Enhanced support for Time Series data For in-memory real-time operational analytics on data coming from your sensors and meter devices Benefits Continuous refresh (Trickle Feed) of TimeSeries data to IWA data mart for TimeSeries query acceleration, for any new data inserted/updated to fact tables and dimension tables Allows partitioning of the time series data based on time intervals in the VTI fact tables Improves refresh performance and system availability IBM Corporation

14 Informix Warehouse Accelerator (IWA) Loading Data from External Tables to Data Mart Powerful method for moving data and for specifying transformations of the data External Tables lets you manage as though the data where in Informix permanent table Loads data directly from External Table to IWA Previously data loading method: Benefits External Table->Informix Database-> IWA Avoids overhead of loading data first to Informix Allows customers to use just the IWA for quick analytics Reduces the time and storage needed on the Informix server side IBM Corporation

15 Generic Performance Enhancements Faster operations In-place alter operations on serial data types SERIAL to SERIAL8, SERIAL to BIGSERIAL, SERIAL8 to BIGSERIAL, BIGSERIAL to SERIAL8 In-place alter requires less space than the previously implemented slow alter thus makes the table available for other options faster. Faster queries for the Committed Read Last Committed isolation level Uses read ahead and light scans Dynamic tuning Dynamic private memory caches for CPU virtual processors (CPUVP) Private memory cache for CPUVP changes size automatically as needed Initial value set by VP_MEMORY_CACHE_KB parameter onstat -g vpcache command displays the target size 15

16 Sensor Data ( Time series) Support for TimeSeries data in a high availability cluster Enables TimeSeries data replication across clusters hosted in read-only mode. Supported on High-Availability Data Replication (HDR), shared-disk secondary and remote stand-alone secondary. Usability Improvement Allows the use of TimeSeries columns in an ORDER BY clause statement. Performance improvement Time series interval data aggregation made faster by including start and end dates in the TSRollup() function. Enables parallelizing queries on virtual tables created using fragment by expression. Loader: New tunable functions TSL_FlushAll() and TSL_Commit() for flushing data to disk while loading. TSL_FlushAll(): Flushes in one transaction. TSL_Commit() : Flushes multiple transactions, as per commit interval IBM Corporation

17 The WIN Factor Highlights of xC2 Release Have the best of both worlds NoSQL and Traditional Relational Both relational and non-relational data in one system Full compatibility with MongoDB APIs all MongoDB applications will connect with Informix with minimal/no changes Access to Informix relational tables from within the NoSQL/MongoDB operators/applications Existing relational (SQL) applications can access JSON/NoSQL data with changes to the applications Real-time operational analytics on Sensor Data at warp speeds With the continuous refresh and partition refresh support for TimeSeries data to IWA data-mart for query acceleration IBM Corporation