{"id":4795,"date":"2025-07-16T10:54:53","date_gmt":"2025-07-16T07:54:53","guid":{"rendered":"https:\/\/www.certbolt.com\/certification\/?p=4795"},"modified":"2026-05-13T08:14:01","modified_gmt":"2026-05-13T05:14:01","slug":"optimizing-database-performance-a-comprehensive-exploration-of-sql-indexing","status":"publish","type":"post","link":"https:\/\/www.certbolt.com\/certification\/optimizing-database-performance-a-comprehensive-exploration-of-sql-indexing\/","title":{"rendered":"Optimizing Database Performance: A Comprehensive Exploration of SQL Indexing"},"content":{"rendered":"

SQL indexing is one of the most powerful and frequently misused tools available to database administrators and developers. At its core, an index is a data structure that the database engine maintains alongside a table to allow rows to be located quickly without scanning every record in the table from beginning to end. The performance difference between a query that uses an appropriate index and one that does not can be measured in orders of magnitude \u2014 what takes seconds with a full table scan can take milliseconds with a proper index, and what takes minutes on a large production database can become nearly instantaneous. This performance gap makes indexing one of the highest-leverage activities in database optimization work.<\/span><\/p>\n

The challenge with SQL indexing is that it is not simply a matter of adding more indexes and watching performance improve. Every index consumes storage space, and every write operation \u2014 every insert, update, and delete \u2014 requires the database to update all indexes on the affected table, adding overhead that accumulates at scale. A table with too few indexes suffers from slow reads; a table with too many indexes suffers from slow writes. Finding the right balance requires understanding how different types of indexes work, how the query optimizer uses them, how to read execution plans that reveal what the optimizer is actually doing, and how to measure the real-world impact of indexing decisions on both read and write performance. This article covers all of these dimensions in practical depth.<\/span><\/p>\n

What an Index Actually Is Inside a Database Engine<\/b><\/h3>\n

To use indexes effectively, it helps to understand what they actually are at a structural level rather than treating them as a black box. The most common index type in relational databases is the B-tree index, named after the balanced tree data structure it is built on. A B-tree index stores index key values in a sorted, hierarchical structure of nodes, where each node contains a range of key values and pointers to child nodes that contain more specific ranges. The leaf nodes at the bottom of the tree contain the actual index key values along with pointers \u2014 either row identifiers or, in clustered indexes, the actual row data \u2014 that allow the database engine to locate the corresponding table rows. The tree is kept balanced, meaning that the path from the root to any leaf node is approximately the same length, which ensures that lookups take a consistent and predictable number of node traversals regardless of where in the key range the sought value falls.<\/span><\/p>\n

The practical implication of this structure is that B-tree indexes are extremely efficient for equality lookups \u2014 finding a row where a column equals a specific value \u2014 and for range scans \u2014 finding all rows where a column falls between two values or is greater or less than a threshold. A lookup in a B-tree index on a table with millions of rows requires traversing only a small number of tree levels, typically between three and five even for very large tables, because the branching factor of each node means the tree grows logarithmically rather than linearly with the number of rows. This logarithmic scaling is what makes B-tree indexes so effective \u2014 adding ten times as many rows to a table increases index lookup time by the logarithm of ten, which is approximately one additional node traversal, rather than by a factor of ten as would be the case with a sequential scan.<\/span><\/p>\n

Clustered Indexes and How They Organize Table Storage<\/b><\/h3>\n

A clustered index is fundamentally different from a non-clustered index in that it determines the physical order in which rows are stored in the table. In a clustered index, the leaf nodes of the B-tree contain the actual row data rather than pointers to row data stored elsewhere. This means there can be only one clustered index per table \u2014 the rows can only be physically sorted in one order \u2014 and it means that the choice of clustered index key has significant implications for how data is stored and accessed. In SQL Server and MySQL with InnoDB, tables with a defined primary key automatically have that primary key serve as the clustered index. In PostgreSQL, clustering is explicitly requested rather than automatic, and tables are stored as heap files by default.<\/span><\/p>\n

The performance advantages of clustered indexes are most pronounced for range queries on the clustered key, because the physical adjacency of rows with similar key values means that range scans require reading contiguous pages from disk rather than jumping to scattered locations. A table clustered on a date column, for example, allows a query that retrieves all rows from a specific date range to read those rows from a contiguous region of disk storage, which is far more efficient than reading the same rows from random locations across the table’s storage. This physical locality also benefits buffer pool utilization, because pages that contain adjacent rows in the clustered order are often useful together and can be prefetched effectively. The downside of clustered indexes becomes apparent with random insertions \u2014 inserting a row with a key value that falls in the middle of the existing key range requires inserting it between existing rows, which can cause page splits that fragment storage and degrade performance over time.<\/span><\/p>\n

Non-Clustered Indexes and Their Pointer Structures<\/b><\/h3>\n

Non-clustered indexes are the most common type encountered in everyday database work, and they maintain a separate data structure from the table’s row storage. The leaf nodes of a non-clustered index contain the index key values along with pointers to the actual row data \u2014 these pointers are either row identifiers in heap-organized tables or clustered index key values in tables that have a clustered index. When a query uses a non-clustered index to find rows, the database performs a two-step process: first it traverses the index B-tree to find the matching key values and their associated row pointers, then it uses those pointers to fetch the actual row data from the table. This second step is called a key lookup or bookmark lookup, and on large result sets it can be more expensive than a full table scan because it involves many small random reads rather than one large sequential scan.<\/span><\/p>\n

The performance of non-clustered indexes is therefore highly dependent on the selectivity of the query \u2014 the fraction of rows in the table that match the query conditions. For highly selective queries that return a small number of rows, the two-step process of index traversal plus row lookup is efficient because only a small number of row lookups are needed. For low-selectivity queries that return a large fraction of the table, the query optimizer may correctly decide that a full table scan is more efficient than an index scan followed by many individual row lookups. Understanding this selectivity threshold, which varies by table size and storage characteristics, helps database developers make informed predictions about when an index will be used and when the query optimizer will choose to ignore it even when it exists.<\/span><\/p>\n

Composite Indexes and the Column Ordering Principle<\/b><\/h3>\n

A composite index, also called a multi-column index, is an index defined on two or more columns rather than a single column. Composite indexes can be far more effective than single-column indexes for queries that filter on multiple columns, because a well-designed composite index can satisfy a multi-column filter entirely from the index without needing to look up rows in the base table at all. However, composite indexes have a critical constraint that is essential to understand: the index can only be used efficiently when queries filter on a prefix of the index column list. An index defined on columns A, B, and C can efficiently support queries that filter on A alone, on A and B together, or on A, B, and C together, but it cannot efficiently support queries that filter on B alone or C alone without also filtering on A.<\/span><\/p>\n

This prefix constraint, sometimes called the leftmost prefix rule, is the reason that column ordering within a composite index requires careful thought based on the actual queries the index will serve. The general guidance is to place the most selective column \u2014 the one that eliminates the most rows when filtered \u2014 first in the index definition, followed by less selective columns. Columns used in equality conditions in queries should typically precede columns used in range conditions, because once a range condition is applied, the index cannot be used for the subsequent columns in the key. Violating this ordering principle produces indexes that appear to cover the needed queries but are used less efficiently by the query optimizer than a properly ordered index would be. Analyzing the actual query patterns against a table before defining composite indexes, rather than guessing at what combinations of columns will be useful, is an essential discipline in composite index design.<\/span><\/p>\n

Covering Indexes and the Concept of Index-Only Scans<\/b><\/h3>\n

A covering index is an index that contains all the columns a query needs to satisfy its result, meaning the database can answer the query entirely from the index without ever touching the base table. When a query can be answered from an index alone, it performs what is called an index-only scan or a covering index scan, which is significantly faster than an index scan followed by row lookups because it eliminates the random read I\/O of fetching rows from the table. Covering indexes are one of the most powerful optimization techniques available for read-heavy workloads, and identifying opportunities to convert an index lookup plus row fetch into a covering index scan is one of the highest-value activities in query performance tuning.<\/span><\/p>\n

Designing a covering index requires knowing precisely which columns a query selects, filters on, and sorts by, and including all of them in the index. The key columns of the index should be the columns used in filter and join conditions, since these determine how the index is traversed, while additional columns needed to complete the SELECT output can be included as non-key included columns \u2014 a feature available in SQL Server, PostgreSQL, and other major databases that allows columns to be stored in the index leaf nodes without being part of the sort key. This included columns feature makes it possible to create covering indexes without unnecessarily widening the index key, which would increase the storage overhead and reduce the branching factor of the B-tree. Balancing the performance benefits of covering indexes against their increased storage cost and write overhead requires quantitative analysis of query frequency, query importance, and the write-to-read ratio of the affected table.<\/span><\/p>\n

Partial Indexes for Focused Optimization on Data Subsets<\/b><\/h3>\n

A partial index, also called a filtered index in SQL Server, is an index that covers only a subset of rows in a table, defined by a WHERE clause that specifies which rows should be included in the index. Partial indexes are a powerful but underutilized optimization tool that allows index size and maintenance overhead to be reduced dramatically when queries are consistently focused on a specific subset of a table’s data. An orders table with millions of historical records but active queries focused almost exclusively on orders with a status of pending or processing is a classic candidate for a partial index on the status column that includes only those active statuses \u2014 the index covers the rows that queries actually need while excluding the much larger population of completed historical orders.<\/span><\/p>\n

The benefits of partial indexes extend beyond the obvious storage savings. A smaller index fits more easily in the database buffer pool, which means index pages are more likely to already be cached in memory when needed rather than requiring a disk read. The reduced size also means that index scans cover fewer pages, further improving query performance. Write operations still need to update the partial index when affected rows fall within the index’s filter condition, but rows outside the filter condition require no index maintenance at all, reducing the per-row write overhead for bulk operations on non-indexed subsets of the data. PostgreSQL has particularly flexible support for partial indexes with arbitrary WHERE conditions, and taking advantage of this flexibility in workloads with natural data segmentation can produce meaningful and durable performance improvements.<\/span><\/p>\n

Functional and Expression Indexes for Transformed Column Values<\/b><\/h3>\n

Standard B-tree indexes store the values of columns as they appear in the table, which means they cannot be used to accelerate queries that apply functions or transformations to column values in their WHERE clauses. A query that filters on LOWER(email) = ‘user@example.com’ cannot use a standard index on the email column because the index stores the original mixed-case values while the query is searching for the lowercased version. A functional index, also called an expression index, solves this problem by storing the result of a specified function or expression rather than the raw column value, allowing queries that use the same function in their filter conditions to benefit from index acceleration.<\/span><\/p>\n

Functional indexes are particularly valuable for case-insensitive string searches, date component extractions, and computed business logic that is applied consistently across queries. An application that consistently searches customer records using lowercased email addresses, for example, can define an expression index on LOWER(email) and immediately gain index acceleration for all such queries without requiring application-level changes to ensure consistent case handling. Similarly, a reporting database that frequently queries on the year or month component of a timestamp column can define expression indexes on EXTRACT(YEAR FROM created_at) and EXTRACT(MONTH FROM created_at) to accelerate these queries dramatically. The maintenance overhead of functional indexes is comparable to standard indexes on columns with similar cardinality, making them a broadly applicable tool for optimizing queries against transformed column values without denormalizing the underlying data.<\/span><\/p>\n

How the Query Optimizer Decides Whether to Use an Index<\/b><\/h3>\n

The query optimizer is the component of the database engine responsible for evaluating available access paths for a query \u2014 table scans, index scans, index seeks, and various join strategies \u2014 and selecting the execution plan it estimates will be most efficient. The optimizer bases its decisions primarily on statistics: metadata about the distribution of values in table columns and indexes that allows it to estimate how many rows a given filter condition will return. If the optimizer estimates that a filter condition is highly selective \u2014 returning a small fraction of table rows \u2014 it will typically choose an index seek. If it estimates low selectivity, it will typically choose a table scan. The accuracy of these estimates depends on the freshness and granularity of the statistics the optimizer is working with.<\/span><\/p>\n

Statistics can become stale after large data modifications, and stale statistics lead to poor optimizer choices \u2014 the optimizer may estimate high selectivity for a column whose value distribution has shifted since statistics were last updated, leading it to choose an index seek for a query that actually returns a large fraction of the table and would be served better by a sequential scan, or vice versa. Most database systems have automatic statistics update mechanisms, but in high-throughput environments where data changes rapidly, automatic updates may not keep pace with actual data distribution changes. Understanding how to monitor statistics age and trigger manual updates when necessary is an important operational skill for database administrators working in performance-sensitive environments. In PostgreSQL, the pg_stats system catalog view provides detailed statistics information; in SQL Server, the sys.dm_db_stats_properties function reports statistics update history.<\/span><\/p>\n

Reading Execution Plans to Diagnose Indexing Problems<\/b><\/h3>\n

An execution plan is the database engine’s detailed record of how it actually executed or plans to execute a query, including which access paths it used, which join strategies it chose, how many rows it estimated at each step, and how many rows it actually processed. Reading execution plans is the most direct way to diagnose indexing problems because it reveals exactly what the optimizer decided to do and why \u2014 or more precisely, what estimated row counts led it to make the decisions it made. All major database systems provide tools for viewing execution plans: the EXPLAIN and EXPLAIN ANALYZE commands in PostgreSQL and MySQL, the SET STATISTICS commands and graphical query plan viewer in SQL Server Management Studio, and equivalent tools in Oracle and other databases.<\/span><\/p>\n

The key indicators of indexing problems in execution plans are full table scans \u2014 labeled as Seq Scan in PostgreSQL or Table Scan in SQL Server \u2014 on large tables in queries where the filter conditions appear selective, nested loop joins where the inner table is accessed without an index seek, and operations labeled as key lookups or bookmark lookups that indicate the optimizer is using an index but then fetching rows from the base table for every matching row. Each of these patterns suggests a specific type of indexing intervention \u2014 adding a missing index, widening an existing index to cover the lookup columns, or revising a composite index key order to better match query filter patterns. Developing the habit of reviewing execution plans for slow queries before attempting any optimization is the discipline that separates methodical performance work from guesswork.<\/span><\/p>\n

Index Fragmentation and the Need for Regular Maintenance<\/b><\/h3>\n

Index fragmentation occurs when the logical order of pages in an index no longer matches their physical order on disk, or when index pages contain large amounts of empty space due to deletions and page splits. Both types of fragmentation \u2014 external fragmentation from disordered physical pages and internal fragmentation from underutilized pages \u2014 degrade query performance because they reduce the efficiency of sequential read operations that the database engine uses when scanning ranges of index values. External fragmentation is particularly harmful for range scans and index-ordered operations because it converts what should be a sequential read into a series of random reads, significantly increasing I\/O cost.<\/span><\/p>\n

Fragmentation accumulates naturally through the normal operation of databases that experience insert, update, and delete activity, and managing it requires periodic index maintenance through rebuild or reorganize operations. Rebuilding an index drops and recreates it from scratch, producing a perfectly ordered, fully packed index with no fragmentation, but it requires a significant amount of I\/O and CPU resources and may lock the index during the rebuild in some database systems and configurations. Reorganizing an index defragments it in place with less resource consumption and no locking in most implementations, but it only addresses external fragmentation without filling underutilized pages. Most database maintenance strategies combine regular reorganization of moderately fragmented indexes \u2014 typically those between fifteen and thirty percent fragmented \u2014 with full rebuilds of heavily fragmented indexes, scheduled during low-traffic windows to minimize impact on production workloads.<\/span><\/p>\n

Monitoring Index Usage to Identify Redundant and Missing Indexes<\/b><\/h3>\n

Database systems accumulate indexes over time as developers add them to address specific performance problems, and without systematic review, the index portfolio on busy tables often grows to include indexes that are rarely or never used in practice. Redundant indexes consume storage, add write overhead, and provide no compensating query performance benefit. Identifying and removing them is an important part of ongoing database performance management. All major database systems provide system views and dynamic management views that track how frequently each index has been used for seeks, scans, and lookups since the database was last restarted, giving administrators the data they need to identify candidates for removal.<\/span><\/p>\n

In SQL Server, the sys.dm_db_index_usage_stats view reports seek, scan, and lookup counts for every index in every database, and indexes with zero seeks and zero lookups over a representative monitoring period are strong candidates for removal after confirming they are not needed for any infrequently executed but business-critical queries. PostgreSQL provides similar information through the pg_stat_user_indexes view. The same system views that identify unused indexes also help identify missing indexes \u2014 by correlating tables with high scan counts against the query patterns that cause those scans, administrators can identify columns that are frequently filtered but not indexed and evaluate whether adding an index would improve performance. SQL Server’s sys.dm_db_missing_index_details view goes further by explicitly suggesting indexes the optimizer considered but did not find during query execution.<\/span><\/p>\n

The Impact of Indexes on Write Performance and Insert Throughput<\/b><\/h3>\n

Every index on a table creates additional work for every write operation \u2014 every INSERT must add a new entry to every index on the table, every DELETE must remove the corresponding entries from every index, and every UPDATE to an indexed column must remove the old index entry and insert a new one. On tables with many indexes, this overhead accumulates significantly and can become the primary bottleneck for write-heavy workloads. Understanding the quantitative relationship between index count and write performance degradation requires measurement in the specific environment, because it depends on factors including the number and size of indexes, the hardware I\/O capabilities, the database’s buffer pool size, and the write concurrency patterns of the application.<\/span><\/p>\n

Bulk load operations \u2014 inserting large quantities of rows into a table at once \u2014 are particularly affected by index overhead. Loading data into a table with many indexes is dramatically slower than loading into a table with fewer indexes, because each inserted row triggers maintenance of every index. Common optimization strategies for bulk loads include temporarily disabling non-clustered indexes before the load and rebuilding them afterward, which allows the rebuild operation to sort and insert index entries in a single efficient pass rather than inserting them one at a time in random order. In environments where tables experience both high read query loads and high insert throughput, partitioning strategies that route inserts to specific partitions while index scans cover the full table provide another approach to managing the tension between read and write performance requirements.<\/span><\/p>\n

Index Design Strategies for Different Workload Patterns<\/b><\/h3>\n

Different application workload patterns create different indexing requirements, and index design that works well for one pattern may perform poorly for another. Online transactional processing workloads \u2014 characterized by many short queries that each access a small number of rows, with a mix of reads and writes occurring simultaneously \u2014 benefit most from narrow, highly selective indexes on columns used in primary key lookups and foreign key joins, with minimal additional indexes to keep write overhead low. Online analytical processing workloads \u2014 characterized by complex queries that scan large portions of tables and perform aggregations \u2014 benefit from wider covering indexes and potentially column store indexes that store data column-by-column rather than row-by-row, enabling highly efficient compression and vectorized aggregation operations.<\/span><\/p>\n

Mixed workloads that combine transactional and analytical query patterns present the most complex indexing challenge, because the indexing choices that optimize analytical reads can significantly degrade transactional write performance. Database systems that separate read replicas from write primaries \u2014 a common architecture in modern cloud database deployments \u2014 allow different indexing strategies to be applied to the read and write tiers independently, with the read replica carrying more and wider indexes to optimize analytical query performance while the write primary carries only the indexes essential for maintaining referential integrity and supporting the most critical transactional queries. This architectural approach to the read-write tension in indexing is increasingly common and worth considering as a solution when indexing optimizations alone cannot simultaneously satisfy the performance requirements of both workload types.<\/span><\/p>\n

Testing Indexing Changes in a Safe and Methodical Way<\/b><\/h3>\n

Indexing changes on production databases require a careful and methodical approach because their effects are not always predictable and can be negative as well as positive. Adding an index can improve the performance of the queries it was designed to help while simultaneously degrading the performance of other queries that the optimizer now chooses to use the new index for, or slowing down writes that previously needed to maintain fewer indexes. Dropping an index can eliminate write overhead and improve bulk operation performance while causing catastrophic query performance degradation if it turns out the index was being used by queries that were not adequately monitored before the change. Testing indexing changes in a staging environment that accurately reflects production data volumes, data distributions, and query patterns is essential before deploying changes to production.<\/span><\/p>\n

When a staging environment is not available or does not adequately represent production conditions, incremental approaches can reduce risk. Adding indexes rather than dropping them first allows their benefit to be measured before any potentially harmful removal is attempted. Database systems that support creating indexes concurrently \u2014 PostgreSQL’s CREATE INDEX CONCURRENTLY, for example \u2014 allow new indexes to be added without locking the table, which makes it possible to test the impact of new indexes in production with limited risk. Monitoring execution plans and query performance metrics before and after an indexing change, using database performance monitoring tools that capture historical query statistics, provides the evidence needed to confirm that a change had its intended effect and did not produce unexpected negative side effects on other parts of the workload.<\/span><\/p>\n

Practical Indexing Recommendations for Common Query Patterns<\/b><\/h3>\n

Translating indexing principles into concrete recommendations for common query patterns encountered in real application development requires both the theoretical knowledge of how indexes work and practical judgment about which patterns are worth optimizing and how. Foreign key columns are one of the most consistently underindexed categories in relational databases \u2014 join operations between parent and child tables are nearly universal in relational applications, and the absence of indexes on foreign key columns causes full table scans on the child table for every join, which becomes a significant problem as the child table grows. Adding indexes on all foreign key columns in tables that will grow to significant size is a broadly applicable default recommendation that rarely causes problems and frequently prevents them.<\/span><\/p>\n

Columns used in ORDER BY and GROUP BY clauses are another commonly overlooked indexing target. When a query sorts its output by a column, the database must either use an index that already stores values in the required order or perform a sort operation on the query result, which is expensive for large result sets. If the column being sorted is also filtered in the query’s WHERE clause, an index that covers both the filter and the sort \u2014 with the filter column leading and the sort column following in the index definition \u2014 can allow the database to return results in the required order directly from the index without any separate sort operation. Similarly, columns used in GROUP BY aggregations benefit from indexing because they allow the database to scan the index in group order and aggregate each group incrementally rather than sorting the full result set. Building the habit of reviewing ORDER BY and GROUP BY clauses alongside WHERE clauses when designing indexes for new queries closes a gap that many developers leave open by focusing exclusively on filter predicates.<\/span><\/p>\n

Conclusion<\/b><\/h3>\n

SQL indexing is not a one-time optimization activity \u2014 it is an ongoing practice that requires continuous attention as application query patterns evolve, data volumes grow, and business requirements shift. The indexes that serve a database well when it contains ten million rows may be inadequate or misaligned when it contains a billion rows, because the relative selectivity of different filter conditions changes as data distributions shift, and the query patterns that dominate a workload change as applications are enhanced and business processes evolve. Building a sustainable indexing practice means treating indexes as living artifacts that require monitoring, measurement, and periodic revision rather than permanent infrastructure that can be installed once and forgotten.<\/span><\/p>\n

The foundation of an effective indexing practice is systematic measurement. Query performance metrics, execution plan analysis, index usage statistics, and fragmentation monitoring together provide the information needed to make informed indexing decisions rather than guesses. Without this measurement infrastructure, indexing becomes a reactive process of responding to performance complaints with ad hoc changes that may improve some queries while degrading others. With it, indexing becomes a proactive discipline of continuously aligning the index portfolio with the workload’s actual patterns and priorities. Investing in the tooling and processes needed to capture and analyze this data \u2014 whether through native database system views, third-party monitoring platforms, or custom instrumentation \u2014 is as important as any specific indexing technique.<\/span><\/p>\n

The conceptual framework developed throughout this article \u2014 from the B-tree structures that make index lookups logarithmically efficient, through the clustered and non-clustered index types and their distinct performance characteristics, to the composite, covering, partial, and functional index patterns that address specific optimization needs \u2014 provides a vocabulary and a mental model for approaching indexing decisions analytically. Applying this framework requires both understanding the principles and developing the practical judgment that comes from working with real databases, observing real query behavior, and accumulating experience with the consequences of different indexing choices across different workload contexts.<\/span><\/p>\n

For database developers and administrators at any stage of their careers, deepening their indexing knowledge pays dividends that compound over time. Databases are central infrastructure in virtually every modern application, and the ability to diagnose and resolve database performance problems is a skill that is consistently valued across industries and roles. The professionals who invest in genuine depth of understanding \u2014 who read execution plans fluently, who can reason about the optimizer’s choices, who understand the storage structures underlying their indexes, and who maintain the discipline of measurement-driven decision making \u2014 consistently deliver better results than those who rely on heuristics and intuition alone. SQL indexing is not the most glamorous topic in the technology field, but in terms of practical impact on the systems that power real businesses and real user experiences, few technical skills deliver more consistent and more measurable value.<\/span><\/p>\n

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SQL indexing is one of the most powerful and frequently misused tools available to database administrators and developers. At its core, an index is a data structure that the database engine maintains alongside a table to allow rows to be located quickly without scanning every record in the table from beginning to end. The performance difference between a query that uses an appropriate index and one that does not can be measured in orders of magnitude \u2014 what takes seconds with a full […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[1018,1027],"tags":[],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.certbolt.com\/certification\/wp-json\/wp\/v2\/posts\/4795"}],"collection":[{"href":"https:\/\/www.certbolt.com\/certification\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.certbolt.com\/certification\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.certbolt.com\/certification\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.certbolt.com\/certification\/wp-json\/wp\/v2\/comments?post=4795"}],"version-history":[{"count":4,"href":"https:\/\/www.certbolt.com\/certification\/wp-json\/wp\/v2\/posts\/4795\/revisions"}],"predecessor-version":[{"id":10269,"href":"https:\/\/www.certbolt.com\/certification\/wp-json\/wp\/v2\/posts\/4795\/revisions\/10269"}],"wp:attachment":[{"href":"https:\/\/www.certbolt.com\/certification\/wp-json\/wp\/v2\/media?parent=4795"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.certbolt.com\/certification\/wp-json\/wp\/v2\/categories?post=4795"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.certbolt.com\/certification\/wp-json\/wp\/v2\/tags?post=4795"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}