![]() ![]() Each compute node has its own dedicated CPU, memory, and attached disk storage, which is determined by the node type.Compute nodes execute the compiled code and send intermediate results back to the leader node for final aggregation.Leader node compiles code for individual elements of the execution plan and assigns the code to individual compute nodes.All other queries run exclusively on the leader node. Leader node distributes SQL statements to the compute nodes only when a query references tables that are stored on the compute nodes.Based on the execution plan, the leader node compiles code, distributes the compiled code to the compute nodes, and assigns a portion of the data to each compute node.It parses and develops execution plans to carry out database operations.Leader node manages communications with client programs and all communication with compute nodes.Compute nodes are transparent to external applications.Client applications interact directly only with the leader node.If a cluster is provisioned with two or more compute nodes, an additional leader node coordinates the compute nodes and handles external communication.Cluster is composed of one or more compute nodes.Core infrastructure component of a Redshift data warehouse.Compiling the query decreases the overhead associated with an interpreter and therefore increases the runtime speed, especially for complex queries. Leader node distributes fully optimized compiled code across all of the nodes of a cluster.Result caching is transparent to the user.If a match is found in the result cache, Redshift uses the cached results and doesn’t run the query. ![]() When a user submits a query, Redshift checks the results cache for a valid, cached copy of the query results.Redshift caches the results of certain types of queries in memory on the leader node.Redshift query run engine incorporates a query optimizer that is MPP-aware and also takes advantage of columnar-oriented data storage.automatically samples the data and selects the most appropriate compression scheme, when the data is loaded into an empty table.doesn’t require indexes or materialized views and so uses less space than traditional relational database systems.employs multiple compression techniques and can often achieve significant compression relative to traditional relational data stores.Columnar data stores can be compressed much more than row-based data stores because similar data is stored sequentially on a disk.columnar data is stored sequentially on the storage media, and require far fewer I/Os, greatly improving query performance.organizes the data by column, as column-based systems are ideal for data warehousing and analytics, where queries often involve aggregates performed over large data sets.makes it easy to add nodes to the data warehouse and enables fast query performance as the data warehouse grows.automatically distributes data and query load across all nodes.Redshift can be easily enabled to a second region for disaster recovery.Redshift provides Audit logging and AWS CloudTrail integration.Redshift provides monitoring using CloudWatch and metrics for compute utilization, storage utilization, and read/write traffic to the cluster are available with the ability to add user-defined custom metrics.However, Multi-AZ deployments are now supported for some types. Redshift supported only Single-AZ deployments before and the nodes are available within the same AZ, if the AZ supports Redshift clusters.supports VPC, SSL, AES-256 encryption, and Hardware Security Modules (HSMs) to protect the data in transit and at rest.distributes & parallelize queries across multiple physical resources.scale up or down with a few clicks in the AWS Management Console or with a single API call.uses replication and continuous backups to enhance availability and improve data durability and can automatically recover from node and component failures.provide fast querying capabilities over structured and semi-structured data using familiar SQL-based clients and business intelligence (BI) tools using standard ODBC and JDBC connections.significantly lowers the cost of a data warehouse, but also makes it easy to analyze large amounts of data very quickly.monitors the nodes and drives to help recovery from failures.patches and backs up the data warehouse, storing the backups for a user-defined retention period. ![]()
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