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How to use the Teradata MAPS Architecture? In an earlier article (link to All Rows on One AMP – By Design), I showed an alternative design for small reference tables, ensuring that all rows in the table are stored on a single Teradata AMP using the Teradata MAPS architecture. The design allows for single-AMP retrieval
What is the Teradata Primary Index? The Teradata primary index is a mechanism that decides where each data row is physically located. It may be based on a single column or multiple columns. The values of the primary index columns may be unique or non-unique. Don’t confuse the primary index with the primary key of
Quick Navigation 1. Complete and up-to-date Statistics2. Teradata Primary Index Choice3. Teradata SQL Tuning with Indexing & Partitioning4. Query Rewriting5. Teradata SQL Tuning with Real-Time Monitoring6. Comparison of Resource Usage 1. Complete and up-to-date Statistics Teradata SQL Tuning begins by providing the optimizer with the statistics it needs. This must always be done as a
The Essential Facts About NOPI Tables in Teradata Teradata NOPI Table Limitations No SET tables allowed No Identity columns allowed No Row-Level Partitioning allowed No UPDATE, MERGE INTO, or UPSERT allowed No Permanent Journaling possible Can’t be defined as Queue Tables No UPDATE triggers allowed No Hash Index allowed No Multiload, as its load algorithm
ALTER Table versus INSERT INTO Changing the structure of a Teradata table can require a lot of resources for large tables. In principle, two methods are available. We can modify the DDL using ALTER TABLE or create an empty table with the desired DDL statement and copy the data using the Teradata INSERT INTO statement.
The initial situation without any index In this blog post, I will show you how you can optimize a query step by step using the right tools that Teradata offers you. Let’s start with the following test scenario: CREATE TABLE Orders ( OrderId BIGINT NOT NULL, CustomerId BIGINT NOT NULL, Amount BIGINT ) PRIMARY INDEX
How Advanced Is The Teradata Columnar Solution? A Column Store is basically characterized by 3 properties: Improved compression compared to Row Stores. This results from the fact that considerably better compression rates can be achieved by packing columns into data blocks. Since the values of a column come from a domain, the entropy is much
With modern cloud databases, such as Snowflake, elasticity is always emphasized as essential. It is cited as a major difference to on-premise shared-nothing databases like Teradata. Mainly the lower cost (“pay only what you need”) is cited as one of the main advantages. I think this is a very one-sided approach, and databases like Snowflake