Storage Array: Holds the data disks, which are high density and high performance.
Linux Hosts (2): Configured for high availability. They compile SQL queries into executable code segments called snippets and create optimized query plans by distributing the snippets across all nodes.
FPGA: A Netezza proprietary hardware tool that filters out unwanted data as early as possible when a SQL query is submitted to the hosts, improving performance.
S-Blades (Intelligent Processing Nodes): Contain powerful multi-core CPUs, multi-engine FPGAs, and gigabytes of RAM. They make up the MPP engine of the Netezza data warehouse appliance.
KVM: Not explicitly mentioned in the text, but typically used for system management tasks.
Data Distribution:
Data is distributed evenly across all disks using either hash or random algorithms. A mirror copy of each slice of data is maintained on a different disk drive if mirroring is enabled. The disk enclosures are connected to the S-Blades via high-speed interconnects.
Key Features:
Supports both Business Intelligence and advanced analytics
Scalable performance at petascale
Efficient for thousands of users simultaneously
Uses i-Class technology for analytic development
Streaming architecture based on blades
Ubiquitous simplicity of deployment and management
Data compliant
Compatible with popular Business Intelligence and analytic tools
Supports standard SQL, ODBC, JDBC, and OLE DB interfaces
Reliability and availability at 99.99% uptime level
Green orientation with low cooling and power requirements
High load pace - over 2 TB of data per hour
High backup creating pace - over 4 TB of data per hour
TwinFin vs. Previous Models:
TwinFin uses S-blades that include CPU, memory, and FPGA (a new term coined by Netezza: database accelerator card = FPGA + memory + IO interface); storage is separated and located in a storage array.
Understanding Netezza TwinFin Architecture
Netezza TwinFin is a massively parallel database system from IBM that offers superior performance for complex analytical workloads. This article provides an overview of the unique TwinFin architecture and its key components.
TwinFin Architecture Components
Processing Nodes: The primary component of the TwinFin system, processing nodes consist of CPUs, GPUs, and memory. Each node can process data independently, allowing for massively parallel processing.
Accelerator Nodes: Accelerator nodes contain custom IBM PowerPC chips that handle specialized tasks, such as compression and encryption, freeing up the CPUs to focus on analytical workloads.
Storage Nodes: Storage nodes provide high-speed storage for data. They can be configured in a variety of ways, depending on the specific needs of the system.
Interconnect: The interconnect is responsible for the communication between processing and storage nodes. It uses proprietary technology to ensure low latency and high throughput.
Massively Parallel Processing
The key advantage of the TwinFin architecture is its ability to process data in a massively parallel manner. Instead of a single CPU processing data sequentially, thousands of CPUs can work on different parts of the same dataset simultaneously.
Example: SQL Query Execution
SELECT column1, column2
FROM table
WHERE condition;
In a traditional database system, this query would be executed sequentially, scanning through each row of the table to find those that match the specified condition. In TwinFin, the query is broken up into smaller parts, with each part processed by a different processing node. This allows for much faster execution times, especially for large datasets.
Data Compression and Encryption
TwinFin's use of accelerator nodes enables efficient data compression and encryption without significant impact on performance. These capabilities are crucial for managing large datasets in a secure and space-efficient manner.
Summary
The Netezza TwinFin architecture offers a unique approach to data processing, leveraging massively parallel processing, specialized hardware, and efficient data management techniques. This results in superior performance for complex analytical workloads.