What is a Database Index?
An index helps in rapid access of database records by storing pointers to their actual disk location.
What is a Database Index?
Database Index Definition
An index offers an efficient way to quickly access the records from the database files stored on the disk drive. It optimizes the database querying speed by serving as an organized lookup table with pointers to the location of the requested data.
Why is Indexing Used in the Database?
The advancements in database technology allow companies to store terabytes of information efficiently in large databases. However, accessing data quickly from large databases is crucial for business success today. Organizations can rapidly retrieve the desired information from large data volumes with database indexing.
Most databases store data in the form of a record (also known as a tuple) within different tables. Each table should have a specific key or attribute for unique identification, known as the primary key. For instance, the primary key in the employee table in a database can be employee ID, which will be unique in every record. The employee ID index stores these keys systematically, along with a pointer showing the disk location where the actual record is stored. Whenever you execute a query with a specific key value like employee ID, the database will quickly look up the index to locate the record(s) associated with the key instead of checking every record in the table.
Since indexing optimizes query performance, most database management systems support built-in and user-defined indexes. Table columns marked with unique or primary key constraints usually have implicit indexes in most databases. In contrast, user-defined indexes are helpful when most database queries contain non-primary key columns.
Indexing also has certain drawbacks. If not done correctly, it can negatively affect the speed of update and delete operations in large databases, as transactions need to maintain both the tables and the indexes. Additionally, indexes require extra space on the underlying physical storage structures of databases, as well as regularly scheduled preventative maintenance.
How can SQL Index Defragmentation Help Your Database?
Heavily fragmented indexes degrade the performance of your database and the applications running on it. Defragmenting indexes reorganizes the physical storage of the data to match the logical order defined by the index, thus improving the efficiency of data retrieval. SQL index defragmentation involves collecting table and index information, analyzing the data, and then taking appropriate reorganization or rebuild operations. After these operations, post-defragmentation analysis is performed to ensure optimal performance.
Our SQL Sentry Fragmentation Manager helps you make intelligent decisions about index management based on the collected table and index information. You can set different schedules for instances and databases down to the individual table or index level, giving you complete granular control over any defragmentation actions. Additionally, you can explicitly set specific schedules for rebuilds or reorganizations. Several additional settings are available to help you calibrate the actions SQL Sentry takes, including:
- The ability to set the scan level or mode used to obtain fragmentation statistics.
- The ability to set minimum and maximum index size thresholds for the collection of fragmentation data.
- The ability to set reorganization and rebuild fragmentation threshold percentages.
- All index defragmentation settings work within the normal SQL Sentry hierarchy, meaning that settings can be configured at one level and are automatically inherited by objects below it, allowing for easy automation within your environment.
What is SQL Index Tuning?
SQL Index Tuning is a critical process aimed at enhancing the performance of SQL queries by optimizing database indexes. This involves a thorough analysis to identify slow-running queries and evaluate the effectiveness of existing indexes. Key steps include selecting appropriate columns for indexing, considering various index types (e.g., B-tree, hash, full-text), and ensuring indexes are well-maintained through regular rebuilding or reorganizing to minimize fragmentation.
The strategy for choosing indexes is multifaceted, balancing the need for quick data retrieval against the overhead indexes introduced, especially in write-heavy databases. Effective tactics include using covering indexes to eliminate table data access, composite indexes for multi-condition queries, and partial indexes for efficiency in large tables targeting specific data segments.
Continuous monitoring and adjustments are essential as data volumes and query patterns evolve, ensuring that the indexing strategy remains aligned with the database's changing needs. Properly tuned indexes can significantly reduce query execution times and enhance overall application performance, making SQL Index Tuning a pivotal aspect of database administration.
Key Steps in SQL Index Tuning:
- Identify Slow-Running Queries: Use query performance analysis tools like SQL Server Profiler, Extended Events, or third-party tools to pinpoint queries that are slow or resource-intensive.
- Evaluate Existing Indexes: Assess the effectiveness of current indexes using tools such as the Database Engine Tuning Advisor (DTA) or Dynamic Management Views (DMVs), identifying those that are underutilized or causing overhead.
- Select Columns for Indexing: Choose columns based on query patterns, focusing on columns frequently used in WHERE, JOIN, and ORDER BY clauses.
- Consider Index Types: Use B-tree indexes for most general-purpose indexing, hash indexes for exact-match queries, and full-text indexes for searching text fields.
- Maintain Indexes Regularly: Perform index maintenance operations like rebuilding and reorganizing to minimize fragmentation and maintain performance.
Effective Indexing Strategies:
- Covering Indexes: Create indexes that include all the columns needed for a query, reducing the need for accessing the actual table data. This is particularly useful for frequently run queries that select a small subset of columns.
- Composite Indexes: Use multi-column indexes to optimize queries with multiple conditions. Composite indexes are beneficial when queries filter on multiple columns in conjunction.
- Partial Indexes: Create indexes on subsets of data to optimize performance for specific queries on large tables. Partial indexes are useful when only a portion of the table's data is queried frequently.
Continuous Monitoring and Adjustment:
As data volumes and query patterns evolve, continuous monitoring and adjustments are essential. Use database performance monitoring tools to keep track of index performance and make necessary adjustments to ensure the indexing strategy remains aligned with the database's changing needs.
Properly tuned indexes can significantly reduce query execution times and enhance overall application performance, making SQL Index Tuning a pivotal aspect of database administration.
SolarWinds SQL Sentry offers database performance monitoring for SQL Server and Azure SQL databases, providing fast root cause analysis and visibility across the data estate.
Index Architecture and Indexing Methods
Indexes typically use a balanced-tree (B-tree) structure to store data in a sorted manner. While other index structures, such as Oracle’s hash index, are possible, they are less common. B-tree indexes enhance the speed of data search and access operations in a database. The data structure associated with the index has two fields: the first field stores the values of the database column the index is sorting, and the second field stores a group of pointers that help identify the disk location of the column values.
Outlined below are some of the key aspects of database indexes:
Cardinality: is one of the essential aspects you should consider when creating database indexes. A table column contains both unique and non-unique values. The cardinality of an index is the total count of non-repeated values it holds. Cardinality is expressed from the high and low standpoint. In the case of high cardinality, most values in the indexed column are dissimilar. Conversely, most values in the indexed column are repetitive with low cardinality.
For example, assume an employee table with three columns: employee ID, age, and department. The cardinality of the employee ID column with the primary key constraint will be high because every record will have a distinct value for this field. In contrast, the cardinality of the department and age columns will be low because they may contain multiple repetitive values. Creating an index on a low cardinality column is not preferable because it returns multiple records upon querying it, which increases overall query execution time and degrades the database performance.
Selectivity: the cardinality of an index divided by the overall tuples in the index represents selectivity. For example, imagine an employee table has 100 rows, and one of its indexed columns has 50 unique values, which is also the cardinality of the column. The selectivity of the indexed column would be then 50/100 = 0.5. The selectivity of “1” is considered the best as it indicates all values in the index are unique. The selectivity of a column with the primary key constraint is always high because it contains zero similar values.
In contrast, a column having multiple non-unique values has low selectivity. For instance, the gender column in an employee table with 10,000 records will have low selectivity because it contains repetitive values, such as male and female. The selectivity of the gender column would be 2/10000 = 0.0002.
Since most databases focus on selectivity figures to create an ideal query execution plan(s), it is preferable to create indexes on columns with high selectivity. For example, creating an index on the employee name column would be much better than the gender column because most values in the employee name column would be distinct compared to the gender column. Any query using the values of the employee name column in the WHERE clause will return limited records compared with the gender column. It also improves the query response time since the database needs to scan limited records to find the desired data.
Density: helps evaluate the count of repeated values in a table column. It can be derived using the following equation: 1/count of unique values in the column. It also means an index on a high-density column would return more records for any particular query since it contains more duplicate values. Therefore, indexes with high density adversely affect the query execution time. Density is also inversely proportional to the selectivity because a higher selectivity value of an index results in fewer rows in the query output.
For example, assume an employee table with three columns: employee ID, department, and name. Many employees can belong to the same department in this table, but every employee can only have a single ID. Therefore, an index on the employee ID column would be more “selective” than the department column since it has low density or zero duplicate values compared to the department column. Query optimizers in databases, such as SQL Server, utilize density data to determine the expected number of records a column can return upon querying it.
Density, cardinality, and selectivity metrics associated with an index are vital for database query optimizers to create an efficient query execution plan(s). These metrics allow the database to determine whether utilizing indexes is better for record retrieval from the database. For instance, if an index has a low selectivity value, it is often better to retrieve a particular record by scanning the entire table than the index. Scanning a table through an index requires more time and database resources such as server memory and disk I/O. Therefore, avoiding index usage is better if it doesn’t offer considerable performance benefits.
Why is Tuning Important for Oracle Databases?
Tuning queries, indexes, and tables is essential in Oracle database environments for enhancing performance, ensuring resource efficiency, and supporting the scalability of applications. These tuning activities directly influence the operational efficiency of critical business applications. Optimized SQL queries, efficient indexing, and well-structured tables can lead to improved response times and user experiences.
Query tuning involves identifying and rectifying slow-running queries. This process helps organizations use database resources like CPU, memory, and I/O more effectively. Not only does this reduce operational costs, but it also minimizes the need for immediate hardware upgrades or excessive indexing. By optimizing SQL queries, you can significantly enhance the performance of your database, ensuring that applications run smoothly and efficiently.
Index tuning is crucial for maintaining the performance of Oracle databases, especially as they grow in size and complexity. Proper index tuning involves creating, modifying, and maintaining indexes to ensure quick data retrieval without incurring excessive overhead. Efficient indexing strategies help in reducing query execution times and enhancing overall application performance.
Tools like SolarWinds Database Performance Analyzer (DPA) play a crucial role in not just rectifying but proactively identifying potential indexing issues before they impact the system. As a result, databases can better handle increasing workloads without compromising performance.
Beyond performance and cost implications, query, index, and table tuning in Oracle are vital to meeting stringent service level agreements (SLAs) and compliance requirements. Ensuring applications remain available and efficient is crucial for business operations. By proactively and regularly tuning SQL queries, indexes, and tables, you can help optimize overall database performance, enhance user satisfaction, and improve the scalability and reliability of your database systems.
What is a Database Index?
Database Index Definition
An index offers an efficient way to quickly access the records from the database files stored on the disk drive. It optimizes the database querying speed by serving as an organized lookup table with pointers to the location of the requested data.
Why is Indexing Used in the Database?
The advancements in database technology allow companies to store terabytes of information efficiently in large databases. However, accessing data quickly from large databases is crucial for business success today. Organizations can rapidly retrieve the desired information from large data volumes with database indexing.
Most databases store data in the form of a record (also known as a tuple) within different tables. Each table should have a specific key or attribute for unique identification, known as the primary key. For instance, the primary key in the employee table in a database can be employee ID, which will be unique in every record. The employee ID index stores these keys systematically, along with a pointer showing the disk location where the actual record is stored. Whenever you execute a query with a specific key value like employee ID, the database will quickly look up the index to locate the record(s) associated with the key instead of checking every record in the table.
Since indexing optimizes query performance, most database management systems support built-in and user-defined indexes. Table columns marked with unique or primary key constraints usually have implicit indexes in most databases. In contrast, user-defined indexes are helpful when most database queries contain non-primary key columns.
Indexing also has certain drawbacks. If not done correctly, it can negatively affect the speed of update and delete operations in large databases, as transactions need to maintain both the tables and the indexes. Additionally, indexes require extra space on the underlying physical storage structures of databases, as well as regularly scheduled preventative maintenance.
How can SQL Index Defragmentation Help Your Database?
Heavily fragmented indexes degrade the performance of your database and the applications running on it. Defragmenting indexes reorganizes the physical storage of the data to match the logical order defined by the index, thus improving the efficiency of data retrieval. SQL index defragmentation involves collecting table and index information, analyzing the data, and then taking appropriate reorganization or rebuild operations. After these operations, post-defragmentation analysis is performed to ensure optimal performance.
Our SQL Sentry Fragmentation Manager helps you make intelligent decisions about index management based on the collected table and index information. You can set different schedules for instances and databases down to the individual table or index level, giving you complete granular control over any defragmentation actions. Additionally, you can explicitly set specific schedules for rebuilds or reorganizations. Several additional settings are available to help you calibrate the actions SQL Sentry takes, including:
- The ability to set the scan level or mode used to obtain fragmentation statistics.
- The ability to set minimum and maximum index size thresholds for the collection of fragmentation data.
- The ability to set reorganization and rebuild fragmentation threshold percentages.
- All index defragmentation settings work within the normal SQL Sentry hierarchy, meaning that settings can be configured at one level and are automatically inherited by objects below it, allowing for easy automation within your environment.
What is SQL Index Tuning?
SQL Index Tuning is a critical process aimed at enhancing the performance of SQL queries by optimizing database indexes. This involves a thorough analysis to identify slow-running queries and evaluate the effectiveness of existing indexes. Key steps include selecting appropriate columns for indexing, considering various index types (e.g., B-tree, hash, full-text), and ensuring indexes are well-maintained through regular rebuilding or reorganizing to minimize fragmentation.
The strategy for choosing indexes is multifaceted, balancing the need for quick data retrieval against the overhead indexes introduced, especially in write-heavy databases. Effective tactics include using covering indexes to eliminate table data access, composite indexes for multi-condition queries, and partial indexes for efficiency in large tables targeting specific data segments.
Continuous monitoring and adjustments are essential as data volumes and query patterns evolve, ensuring that the indexing strategy remains aligned with the database's changing needs. Properly tuned indexes can significantly reduce query execution times and enhance overall application performance, making SQL Index Tuning a pivotal aspect of database administration.
Key Steps in SQL Index Tuning:
- Identify Slow-Running Queries: Use query performance analysis tools like SQL Server Profiler, Extended Events, or third-party tools to pinpoint queries that are slow or resource-intensive.
- Evaluate Existing Indexes: Assess the effectiveness of current indexes using tools such as the Database Engine Tuning Advisor (DTA) or Dynamic Management Views (DMVs), identifying those that are underutilized or causing overhead.
- Select Columns for Indexing: Choose columns based on query patterns, focusing on columns frequently used in WHERE, JOIN, and ORDER BY clauses.
- Consider Index Types: Use B-tree indexes for most general-purpose indexing, hash indexes for exact-match queries, and full-text indexes for searching text fields.
- Maintain Indexes Regularly: Perform index maintenance operations like rebuilding and reorganizing to minimize fragmentation and maintain performance.
Effective Indexing Strategies:
- Covering Indexes: Create indexes that include all the columns needed for a query, reducing the need for accessing the actual table data. This is particularly useful for frequently run queries that select a small subset of columns.
- Composite Indexes: Use multi-column indexes to optimize queries with multiple conditions. Composite indexes are beneficial when queries filter on multiple columns in conjunction.
- Partial Indexes: Create indexes on subsets of data to optimize performance for specific queries on large tables. Partial indexes are useful when only a portion of the table's data is queried frequently.
Continuous Monitoring and Adjustment:
As data volumes and query patterns evolve, continuous monitoring and adjustments are essential. Use database performance monitoring tools to keep track of index performance and make necessary adjustments to ensure the indexing strategy remains aligned with the database's changing needs.
Properly tuned indexes can significantly reduce query execution times and enhance overall application performance, making SQL Index Tuning a pivotal aspect of database administration.
SolarWinds SQL Sentry offers database performance monitoring for SQL Server and Azure SQL databases, providing fast root cause analysis and visibility across the data estate.
Index Architecture and Indexing Methods
Indexes typically use a balanced-tree (B-tree) structure to store data in a sorted manner. While other index structures, such as Oracle’s hash index, are possible, they are less common. B-tree indexes enhance the speed of data search and access operations in a database. The data structure associated with the index has two fields: the first field stores the values of the database column the index is sorting, and the second field stores a group of pointers that help identify the disk location of the column values.
Outlined below are some of the key aspects of database indexes:
Cardinality: is one of the essential aspects you should consider when creating database indexes. A table column contains both unique and non-unique values. The cardinality of an index is the total count of non-repeated values it holds. Cardinality is expressed from the high and low standpoint. In the case of high cardinality, most values in the indexed column are dissimilar. Conversely, most values in the indexed column are repetitive with low cardinality.
For example, assume an employee table with three columns: employee ID, age, and department. The cardinality of the employee ID column with the primary key constraint will be high because every record will have a distinct value for this field. In contrast, the cardinality of the department and age columns will be low because they may contain multiple repetitive values. Creating an index on a low cardinality column is not preferable because it returns multiple records upon querying it, which increases overall query execution time and degrades the database performance.
Selectivity: the cardinality of an index divided by the overall tuples in the index represents selectivity. For example, imagine an employee table has 100 rows, and one of its indexed columns has 50 unique values, which is also the cardinality of the column. The selectivity of the indexed column would be then 50/100 = 0.5. The selectivity of “1” is considered the best as it indicates all values in the index are unique. The selectivity of a column with the primary key constraint is always high because it contains zero similar values.
In contrast, a column having multiple non-unique values has low selectivity. For instance, the gender column in an employee table with 10,000 records will have low selectivity because it contains repetitive values, such as male and female. The selectivity of the gender column would be 2/10000 = 0.0002.
Since most databases focus on selectivity figures to create an ideal query execution plan(s), it is preferable to create indexes on columns with high selectivity. For example, creating an index on the employee name column would be much better than the gender column because most values in the employee name column would be distinct compared to the gender column. Any query using the values of the employee name column in the WHERE clause will return limited records compared with the gender column. It also improves the query response time since the database needs to scan limited records to find the desired data.
Density: helps evaluate the count of repeated values in a table column. It can be derived using the following equation: 1/count of unique values in the column. It also means an index on a high-density column would return more records for any particular query since it contains more duplicate values. Therefore, indexes with high density adversely affect the query execution time. Density is also inversely proportional to the selectivity because a higher selectivity value of an index results in fewer rows in the query output.
For example, assume an employee table with three columns: employee ID, department, and name. Many employees can belong to the same department in this table, but every employee can only have a single ID. Therefore, an index on the employee ID column would be more “selective” than the department column since it has low density or zero duplicate values compared to the department column. Query optimizers in databases, such as SQL Server, utilize density data to determine the expected number of records a column can return upon querying it.
Density, cardinality, and selectivity metrics associated with an index are vital for database query optimizers to create an efficient query execution plan(s). These metrics allow the database to determine whether utilizing indexes is better for record retrieval from the database. For instance, if an index has a low selectivity value, it is often better to retrieve a particular record by scanning the entire table than the index. Scanning a table through an index requires more time and database resources such as server memory and disk I/O. Therefore, avoiding index usage is better if it doesn’t offer considerable performance benefits.
Why is Tuning Important for Oracle Databases?
Tuning queries, indexes, and tables is essential in Oracle database environments for enhancing performance, ensuring resource efficiency, and supporting the scalability of applications. These tuning activities directly influence the operational efficiency of critical business applications. Optimized SQL queries, efficient indexing, and well-structured tables can lead to improved response times and user experiences.
Query tuning involves identifying and rectifying slow-running queries. This process helps organizations use database resources like CPU, memory, and I/O more effectively. Not only does this reduce operational costs, but it also minimizes the need for immediate hardware upgrades or excessive indexing. By optimizing SQL queries, you can significantly enhance the performance of your database, ensuring that applications run smoothly and efficiently.
Index tuning is crucial for maintaining the performance of Oracle databases, especially as they grow in size and complexity. Proper index tuning involves creating, modifying, and maintaining indexes to ensure quick data retrieval without incurring excessive overhead. Efficient indexing strategies help in reducing query execution times and enhancing overall application performance.
Tools like SolarWinds Database Performance Analyzer (DPA) play a crucial role in not just rectifying but proactively identifying potential indexing issues before they impact the system. As a result, databases can better handle increasing workloads without compromising performance.
Beyond performance and cost implications, query, index, and table tuning in Oracle are vital to meeting stringent service level agreements (SLAs) and compliance requirements. Ensuring applications remain available and efficient is crucial for business operations. By proactively and regularly tuning SQL queries, indexes, and tables, you can help optimize overall database performance, enhance user satisfaction, and improve the scalability and reliability of your database systems.
Cross-platform database monitoring and management software built for SQL query performance monitoring, analysis, and tuning.
The Database Self Hosted License provides access to Database Performance Analyzer or SQL Sentry to monitor and optimize multiple database types for cloud and on-premises environments.
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