1. Statistics

In Teradata, comprehending and overseeing statistics is essential for optimizing database performance. Statistics provide the optimizer with precise data about stored information, allowing for well-informed decisions when handling queries. This article will explore the significance of statistics in Teradata, their effect on query performance, and recommended methods for upkeep.

The Role of Statistics in Teradata

Teradata statistics provide essential data about the distribution of information in tables, columns, and indices. This metadata empowers the optimizer to make informed decisions regarding the optimal processing of inquiries. Precise and current statistics enable the optimizer to:

  1. Choose the most appropriate join strategies and access paths.
  2. Estimate the number of rows that will be returned by a query, facilitating the allocation of appropriate resources.
  3. Determine the optimal order to execute various steps within a query plan.

The Impact of Statistics on Query Performance

Inaccurate statistics can result in suboptimal decision-making by the optimizer, leading to poor query performance. This may entail selecting less efficient join methods or access paths, which can result in longer query execution times and increased resource consumption.

Outdated or incomplete statistics can cause the optimizer to make erroneous assumptions regarding data distribution, resulting in poor performance. As a result, precise and current statistics are critical for achieving optimal query performance.

Maintaining Statistics in Teradata

Adhering to best practices when maintaining statistics is essential to utilize Teradata’s query optimization capabilities fully. The following guidelines will assist you in achieving this objective:

  1. Collect statistics on all columns used in join conditions, WHERE clauses, and GROUP BY clauses. This will help the optimizer decide between join strategies and access paths.
  2. Update statistics regularly, especially after significant data changes, such as data loads, deletes, or updates. This ensures the optimizer has the most current information about the data distribution.
  3. Use the SAMPLE option when collecting statistics to reduce the time and resources required for the process. However, ensure that the sample size is large enough to provide accurate estimates of the data distribution.
  4. To simplify collecting and maintaining statistics, use Teradata’s automatic statistics management features, such as AutoStats.
  5. Monitor the performance of your queries and review the Explain plans to identify potential areas for improvement. If you notice suboptimal query execution plans, consider updating the statistics or adjusting the query.


To optimize Teradata database performance, statistics play a crucial role. By comprehending their significance and adhering to recommended practices for their maintenance, you can guarantee that your database runs smoothly and productively. Consistently managing and updating statistics will assist the optimizer in making informed judgments, ultimately enhancing query performance and minimizing resource usage.

2. Achieving the Perfect Primary Index

Teradata is a robust database management system crafted for large-scale data warehousing. Optimizing Teradata performance involves selecting an ideal Primary Index for your tables. This article highlights the significance of selecting the appropriate Primary Index, its impact on SQL tuning, and best practices for achieving top-notch performance in Teradata.

The Importance of the Primary Index in Teradata

Teradata’s Primary Index determines data distribution and query processing efficiency.

  1. Ensuring an even data distribution across all AMPs (Access Module Processors) is essential for parallel data processing.
  2. Facilitating efficient join operations by ensuring that related rows are co-located on the same AMP.
  3. Minimizing the impact of data volatility on query performance.

Designing Queries for Optimal Primary Index Usage

To maximize the advantages of Teradata’s Primary Index, employ query designs that allow the optimizer to utilize the Primary Index in joins efficiently. Consider the following guidelines to accomplish this:

  1. Ensure that all join columns are part of the Primary Index. If even one column is missing from the join condition, the result will be a different row hash, which can negatively impact query performance.
  2. The join condition can include additional columns, which should be applied as residual conditions after locating the rows via row hash.
  3. Aim for co-location of rows in joined tables by ensuring that both tables have the same Primary Index. This enables the optimizer to use a direct join strategy, which requires no redistribution of rows and significantly improves query performance.

Selecting the Perfect Primary Index

When selecting a Primary Index, three considerations should be considered: even data distribution, compatibility with join operations, and minimal volatility.

  1. Uniform Distribution of Data: An evenly distributed Primary Index ensures that all AMPs start and finish their work simultaneously, maximizing parallel processing capabilities.
  2. Suitability for Join Operations: Choose a Primary Index that facilitates efficient join operations by ensuring that related rows are co-located on the same AMP.
  3. Low Volatility: Select a Primary Index with low volatility to minimize the impact of data changes on query performance.

Creating a volatile table may be beneficial to preserve the Primary Index or when it cannot be modified. The volatile table should have an identical structure and content to the initial table but with a distinct Primary Index.


Optimizing query performance and ensuring efficient data processing in Teradata requires achieving the perfect Primary Index. This can be done by understanding the significance of the Primary Index, adhering to query design best practices, and careful index selection. To maximize the performance of your Teradata database system, factors such as uniform data distribution, join operation suitability, and low volatility should be considered when choosing the ideal Primary Index for your tables.

3. Indexes and Partitioning Strategies

Optimizing query performance is essential for a data warehouse system. Teradata offers diverse indexes and partitioning techniques that can improve query performance. This article delves into the different indexes and partitioning methods in Teradata, their respective pros and cons, and how to select the best approach for your workload.

Types of Indexes in Teradata

Teradata provides various index types, each with distinct advantages and disadvantages. The index selection mainly relies on the workload necessities. Teradata presents two primary secondary index forms: the Unique Secondary Index (USI) and Non-Unique Secondary Index (NUSI).

1. Unique Secondary Index (USI)

The USI resembles the Unique Primary Index. Rows in the Index subtable are evenly dispersed among all AMPs by RowHash and sorted likewise. If the USI encompasses all columns utilized in a WHERE clause, the corresponding AMP can be pinpointed, enabling streamlined access to singular rows. USI is optimal for tactical workloads necessitating exceptional performance.

2. Non-Unique Secondary Index (NUSI)

RowHash does not distribute NUSI rows as USI. They are stored on the same AMP as the corresponding base table row, making NUSI access an all-AMP operation. NUSI index rows are sorted by RowHash or an integer value, making them optimal for range scans.

3. Teradata Row-Partitioned Tables (PPI)

Partitioning is a storage structure method for rows that don’t necessitate additional space, unlike secondary indexes. A partitioned table distributes rows to AMPs according to RowHash, assigns them to appropriate partitions, and sorts them by RowHash within those partitions.

Partitioned tables facilitate full cylinder scans, rendering them ideal for strategic queries that consolidate a set of data. Nevertheless, the decision to opt for partitioning or secondary indexes hinges on the workload. It is also viable to establish supplementary indexes on a partitioned table.

Disadvantages of Indexing and Partitioning

Indexing and partitioning can enhance query performance but may adversely affect the ETL process.

  1. Loading tools like Fastload require removing secondary indexes and join indexes before loading.
  2. Teradata needs to manage index sub-tables, meaning Insert, Delete, and Update statements require maintenance of the index sub-table and the actual table.
  3. Unused indexes waste space and resources. Remove them if they are not helpful in the entire PDM design.


To optimize query performance in Teradata, it’s vital to comprehend the diverse index types and partitioning strategies accessible. When selecting the appropriate approach, contemplate your workload requirements and the potential effect on the ETL process. You can guarantee efficient data processing and enhance query performance in your Teradata data warehouse by executing proper indexing and partitioning techniques.

4. Query Optimization: Techniques and Best Practices

Optimizing queries is crucial for achieving optimal performance in data warehouse systems like Teradata. Although enhancing the query’s business logic may be tempting, prioritizing technical optimizations reduces the risk and minimizes the need to contact the query’s author. This post will explore several methods for improving query performance in Teradata.

Technical Optimization Techniques

Before delving into the query’s business logic, contemplate the subsequent technical optimization techniques:

  1. Use EXISTS instead of IN: Using EXISTS can improve query performance by avoiding full table scans, as it stops searching as soon as a match is found.
  2. Split large SQL statements into smaller parts: Breaking down complex queries into smaller, more manageable pieces can make them easier to optimize and debug.
  3. Use UNION ALL instead of UNION: UNION ALL is faster than UNION, as it does not remove duplicates. Use it when you are sure there are no duplicate records or when duplicates do not impact the results.
  4. Use DISTINCT instead of GROUP BY: DISTINCT can sometimes be more efficient than GROUP BY, especially when removing duplicates from a single column.

These technical improvements can enhance query performance without thoroughly comprehending the query’s business logic.

Business Logic Optimization

Once technical optimizations are exhausted, delving into the query’s business logic is imperative. A more profound comprehension of the business requirements may uncover further possibilities for fine-tuning. Yet, exercise care when altering queries that involve business logic since modifications could affect the precision of outcomes.

Rewriting Queries for Better Performance

Rewriting a query can resolve performance issues, even after exhausting all other optimization techniques. Thoroughly analyze the query structure and explore alternative methods of expressing the same logic to enhance efficiency. This may entail adjusting filter conditions, reorganizing subqueries, or rewriting JOINs.


A combination of technical and business logic optimizations must be implemented to optimize query performance in Teradata. It is recommended to prioritize technical optimizations to reduce the need to consult the query’s author and minimize risk. However, there may be cases where delving into the business logic can uncover additional tuning opportunities. Applying these techniques can lead to a significant improvement in query performance and enhance the overall efficiency of the Teradata data warehouse.

5. Real-Time Query Monitoring and Analysis

Teradata SQL tuning entails monitoring and analyzing queries in real-time to detect and fix performance problems. Teradata Viewpoint provides a holistic approach to monitoring queries during runtime, enabling you to identify crucial steps and implement corrective measures. This article will explore the use of Viewpoint to monitor queries and analyze problematic steps for enhanced performance.

Monitoring Queries in Viewpoint

To effectively monitor a Viewpoint query, pay attention to the following areas:

  1. Steps that take a long time to finish and consume significant resources may indicate inefficiencies in the query or data structure that need to be addressed.
  2. Steps with high skew factors: Skew occurs when data is unevenly distributed across AMPs, causing some AMPs to perform more work than others. A skew factor approaching 99% means that only one AMP or a few AMPs are doing all the work, which can severely impact query performance.

Analyzing Problematic Query Steps

After pinpointing a troublesome query step, analyze the subsequent possible underlying reasons:

  1. Skewed primary index in the base table (static skew): A poorly designed primary index can lead to uneven data distribution and reduced parallelism. Re-evaluating the primary index choice may help resolve this issue.
  2. Redistribution of rows causing skew (dynamic skew): In some cases, redistributing rows during query execution can create skew. Analyzing the query plan and modifying the join conditions or index choices may help to mitigate dynamic skew.
  3. Poor join preparation due to missing or stale statistics: Outdated or missing statistics can lead to incorrect join decisions, causing inefficient data redistribution. Regularly collecting and updating statistics can help to improve join performance.
  4. Hash collisions during the execution of the INSERT statement: Hash collisions occur when multiple rows have the same hash value, leading to contention and reduced performance. Adjusting the primary index or using a different hashing algorithm may reduce collisions.


Teradata’s real-time query monitoring and analysis can enhance performance by identifying and addressing critical steps. Teradata Viewpoint provides a robust tool for monitoring queries and analyzing problematic steps. By focusing on resource-intensive steps and high-skew factors, potential issues can be identified, and corrective actions can be taken to optimize query performance.

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