DESC:FIELD OF INVENTION
[0001] The present disclosure relates to the field of database management system. More specifically, the present disclosure relates to an enhanced database management and optimization system.

BACKGROUND OF THE INVENTION
[0002] Typically, PostgreSQL uses a 32-bit Transaction ID (XID) counter for tracking transactions. The counter can exhaust after approximately 2^31 transactions, causing a wraparound issue and potentially risking database corruption. Conventionally to prevent this, PostgreSQL reserves a special XID, Frozen Transaction Id, which does not follow the normal XID comparison rules and is always considered older than every normal XID. However, despite such methods the user may find it challenging to predict and mitigate the wrap around issue. Oftentimes, the user may have to perform intensive manual intervention to mitigate the risks associated with the wraparound issue. Therefore, there is a need for better tools and methods to mitigate the wraparound issue and potential risks involved for users.

SUMMARY OF THE INVENTION
[0003] In an aspect, a system for database management is disclosed. The system may include a processor and a memory for storing instructions. The system monitors at least one performance metric of the at least one database in real-time based on one or more on-going transactions associated with the at least one database. The at least one performance metric comprises at least one query performance metric, throughput metric, resource utilization metric, storage metric, uptime metric, error tracking metric, schema metric, or any combination thereof. Based on the at least one monitored performance metric or historical data associated with the at least one database, the system predicts, via at least one artificial intelligence (AI) model, in advance at least one anomaly, error, deadlock, resource exhaustion, resource over-utilization, or any combination thereof expected to occur corresponding to the at least one database. The system identifies at least one preemptive action to be performed corresponding to the at least one predicted anomaly, error, deadlock, resource exhaustion, or resource over-utilization, or any combination thereof expected to occur, wherein the at least one identified preemptive action optimizes the at least one monitored performance metric and performs at least one action based on the at least one identified preemptive action.
[0004] In another aspect, a method for database management is disclosed. The method may be implemented by the system and includes the steps of monitoring at least one performance metric of the at least one database in real-time based on one or more on-going transactions associated with the at least one database, predicting via the at least one AI model, in advance at least one anomaly, error, deadlock, resource exhaustion, resource over-utilization, or any combination thereof expected to occur corresponding to the at least one database based on the at least one monitored performance metric, historical data associated with the at least one database, or any combination thereof. Thereafter, at least one preemptive action is identified to be performed corresponding to the at least one predicted anomaly, error, deadlock, resource exhaustion, or resource over-utilization, or any combination thereof and at least one action based on the at least one identified preemptive action is being performed. The at least one identified preemptive action optimizes the at least one monitored performance metric.

BRIEF DESCRIPTION OF THE DRAWINGS
[0005] FIG. 1 is an exemplary illustration of an environment for implementing a database management system, in accordance with which various embodiments of the present disclosure may be implemented.
[0006] FIG. 2 is a schematic block diagram of a system implemented in the environment of FIG. 1, in accordance with which various embodiments of the present disclosure may be implemented;
[0007] FIG. 3 is a schematic illustration of a method for database management, in accordance with the embodiment of the present disclosure.
[0008] FIG. 4 is a schematic illustration of a method for managing generation of unique identifiers in a database, in accordance with the embodiment of the present disclosure.
[0009] FIG. 5 is a schematic illustration of a method for optimizing a database query, in accordance with the embodiment of the present disclosure.
[0010] Skilled artisans will appreciate that elements in the figures are illustrated for simplicity and clarity and have not necessarily been drawn to scale. For example, the dimensions of some of the elements in the figures may be exaggerated relative to other elements to help to improve understanding of embodiments of the present invention.
[0011] The system and method components have been represented where appropriate by conventional symbols in the drawings, showing only those specific details that are pertinent to understanding the embodiments of the present invention so as not to obscure the disclosure with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein.

DETAILED DESCRIPTION
[0012] The wraparound issue in PostgreSQL occurs when transaction IDs (XIDs) are exhausted, leading to a potential data corruption. This problem arises because PostgreSQL uses 32-bit integers for transaction IDs, which means that after approximately 2 billion transactions, the XID counter wraps around to zero. Thereby, this can result in old transactions being considered as new if not managed properly, which could corrupt the database.
[0013] With the enhanced database management and optimization system the wrap around issue can be prevented. The system uses predictive analytics, dynamic XID allocation, and a smart vacuuming process to ensure that transaction IDs are managed efficiently, and wraparound risks are mitigated without the need for an intensive manual intervention.
[0014] The predictive XID exhaustion analytics in postgreSQL is implemented by using a predictive analytics engine that continuously monitors transaction rates and projects when the XID counter is likely to approach the wraparound threshold. This engine uses historical data and current transaction patterns to provide accurate predictions, allowing the system to preemptively take action before the issue becomes critical. The pg_stat_database view in the postgreSQL is used to track the number of transactions over time. The pg_stat_database view contains useful columns like xact_commit and xact_rollback for transaction statistics. The predictive XID exhaustion analytics store the data associated with the transaction statistics at regular intervals (e.g., every minute, hour) to build historical patterns of the transaction rates.
[0015] The predictive XID exhaustion analytics has a mechanism to monitor the age of transaction IDs relative to the wraparound threshold. The age function used to check the XID age of the current transaction also use pg_stat_database to track XID wraparound warnings. Also, a predictive model is built that uses historical transaction data to predict future XID consumption. The model may utilize statistical methods like linear regression, exponential smoothening and machine learning models, and the predictive model is updated based on continuous real time data on transaction rates. Accordingly, the model is set up to provide automatic alerts (email, Slack notifications, etc.) based on the configured time threshold (e.g., notify when the system is 30 days away from potential exhaustion) when the model predicts that a XID wraparound exhaustion is approaching. Also, the model can create custom alert rules that trigger when the XID age crosses a certain threshold or based on the predicted time to exhaustion.
[0016] The preventive actions like automatic vacuuming, database maintenance and scaling infrastructure is also implemented by the predictive XID exhaustion analytics. The automatic vacuuming method ensures the system is configured to run vacuum automatically to freeze old transaction IDs. Also, a user can manually trigger aggressive vacuuming if the predictive model indicates imminent XID exhaustion. The database maintenance method schedules preventive maintenance to freeze XIDs earlier than usual if the predictions indicate a high risk of wraparound. The scaling infrastructure method is based on the predictive model’s output, user may need to scale the infrastructure (e.g., add more instances, improve I/O) to accommodate the increasing transaction rate before exhaustion occurs. Also, the predictive XID exhaustion analytics helps to implement custom dashboard that visualizes the XID consumption trends and projected exhaustion timelines, allowing the user to easily monitor the system health in real-time. Also, the predictive model is automatically reviewed and adjusted based on the change in transaction patterns to maintain accuracy. Also, the model is automatically retrained based on new data, especially if the transaction rates fluctuate significantly over time.
[0017] The dynamic XID allocation in postgreSQL is a mechanism that optimizes the usage of transaction IDs. Instead of assigning XIDs sequentially, the system can use a more distributed approach, such as allocating XIDs in ranges based on transaction types, priorities, or table-specific needs. This method reduces the likelihood of rapid XID exhaustion in high-transaction environments.
[0018] The automated transaction ID (XID) management logic method is used to allocate XIDs dynamically, based on the identified factors (transaction type, priority, table). This method involves various steps like transaction batching and XID ranges, transaction prioritization, addition of intercept logic, leverage GUC (Grand Unified Configuration) parameters, context-aware scheduling and prioritization needs, optimize vacuum operations with load balancing, automatically trigger vacuum for tables nearing XID wraparound, multi-phase transaction management, track dependencies between phases, phase commit and rollback mechanism, phase-level isolation, long-running queries in phases, optimize phase execution for large transactions, phase scheduling and resource management, automatic retry of failed phases, audit and monitoring, design goals for the XID recycling and reclamation engine, monitor and track transaction lifecycles, safe reclamation conditions, edge cases and long-running transactions, and scale XID recycling based on workload.
[0019] The transaction batching and XID ranges step create XID ranges for different transaction types or priorities instead of assigning XIDs sequentially. For example, short-lived transactions may be allocated XIDs in the range of 1 to 1 million and long-running transactions may be assigned XIDs in the range of 1 million to 2 million. Also, the transaction manager is modified to check the transaction type and assign an XID from the appropriate range.
[0020] The transaction prioritization step involves assigning XIDs preferentially to high-priority transactions (e.g., user-facing critical transactions) from a reserved range to ensure they are always processed smoothly without contention for XIDs. Also, the transaction prioritization step implements a custom priority-based XID allocator where XIDs are distributed based on pre-defined transaction importance levels.
[0021] The addition of intercept logic step helps to implement logic to intercept XID allocation requests, inspect the transaction metadata, and assign XIDs based on predefined dynamic rules. Here, before the new XID is assigned in the GetNewTransactionId(), a logic to inspect transaction metadata and apply custom rules is inserted. This could involve looking at attributes like the transaction session user, the type of query being executed (read or write), and the transaction isolation level. Also, a transaction classifier module categorizes transactions at the beginning of each transaction and analyze the query type, transaction source, and associated table to assign a category, and this information can be passed to the XID allocator. The classifier also could be implemented using a trigger on transaction start. This also helps in implementing dynamic rules in the intercept logic, these could be defined in a configuration file or dynamically altered at runtime. The example rules could include always assigning specific XIDs to certain user sessions, grouping of related transactions into a block of XIDs, assigning XIDs based on the size or type of transaction.
[0022] The leverage GUC (Grand Unified Configuration) parameters step facilitates to control aspects of the XID assignment logic. The concurrent transactions are handled without causing deadlocks or race conditions by the XID allocation logic. Also, here for low-priority transactions XID assigned from a common pool that may be more prone to recycling but ensures that high-priority or critical transactions always have a dedicated range. And, for high-priority transactions, XIDs are reserved in a special range or pool to ensure these transactions do not face contention for XIDs. And, for heavy-transaction tables, that is for tables with high write frequencies, a block of XIDs is assigned to such tables to prevent XID contention in shared pools. Also, the detailed logging and monitoring of XIDs allocation data is stored in a dedicated table or log file to track the number of XIDs assigned to different transaction types, priorities, and tables.
[0023] A logging system helps to capture each XID allocation, including which pool the XID came from and the transaction metadata associated with the XID. This allows a user to analyze patterns and adjust the dynamic allocation algorithm as needed and set up a garbage collection mechanism to recycle XID pools once transactions within that range are completed and committed. Also, this can involve checking for frozen transactions in each XID range and freeing up those XIDs for future use. This can also involve automatically evaluating the system under various conditions such as high transaction throughput, long-running bulk transactions, concurrent user activity. Also, this can be responsible in analyzing how XID ranges are consumed and tune the size of the ranges to optimize performance. This may involve tweaking the classifier logic to better categorize transactions and allocating XIDs more efficiently. The dynamic reallocation can be used to redistribute XID pools during runtime based on real-time transaction activity, adaptive pooling can be used to automatically expand or contract XID ranges based on the current system load, and parallel XID allocation can be used to use multiple XID allocation threads to optimize performance under heavy load.
[0024] The context-aware scheduling and prioritization needs step prioritizes XIDs based on critical tables, XID age, update and delete rates, and system load. The critical tables are large, heavily updated, or tables containing critical data that need to be prioritized to maintain database performance. The XID Age helps tables with old transaction IDs (approaching the wraparound threshold) to be vacuumed sooner to prevent XID exhaustion. The update and delete rates help to facilitate vacuuming more frequently for tables with high rates of updates and deletes as such tables accumulate dead tuples faster and need more frequent vacuuming. The system load help to distribute vacuum operations during times of low system activity to avoid performance bottlenecks.
[0025] Also, various parameters are used to fine tune the vacuuming process. The autovacuum_naptime decides how frequently the autovacuum daemon checks for tables that need vacuuming. The autovacuum_vacuum_threshold and autovacuum_ analyze_threshold decides the number of tuples before a table is vacuumed or analyzed. The autovacuum_vacuum_scale_factor and autovacuum_ analyze_ scale_factor decides the percentage of dead tuples required before vacuum or analyze is triggered. The autovacuum_vacuum_cost_limit and autovacuum_vacuum_ cost_delay control the resource usage of vacuum, ensuring it doesn't consume too many I/O resources. Also, a monitoring script checks XID age of the tables, tuple count, and dead tuple count, and then schedules vacuuming based on priority. The system based on the result, prioritizes tables with high xid_age or n_dead_tup, trigger an immediate VACUUM FREEZE if a table's xid_age exceeds a critical threshold. Otherwise, prioritize vacuuming based on the number of dead tuples or table size. A script runs every few minutes to gather vacuuming metrics (XID age, dead tuples, table size) and prioritizes tables accordingly and stores the vacuum priority in a separate table. The vacuum tables with a higher priority are allocated first, and vacuum jobs are distributed over time to avoid overwhelming the system.
[0026] The optimize phase execution for large transactions step distribute vacuum operations evenly over time to avoid I/O bottlenecks caused by vacuum operations if not managed properly. A vacuum cost delay and cost limit helps to fine-tune the vacuum_cost_delay and vacuum_cost_limit settings to control how aggressively vacuum operates. The vacuum_cost_delay indicates the time in milliseconds that the vacuum process will sleep after reaching the cost limit. The vacuum_cost_limit indicates total cost of I/O operations after which vacuum will sleep. Also, this helps to distribute vacuum load rather than running multiple vacuum jobs simultaneously, and schedule vacuum operations to occur sequentially across less busy periods of time (e.g., night hours or periods of low transaction activity).
[0027] The automatically trigger vacuum for tables nearing XID wraparound step helps to set up a watchdog script to continuously monitor the age of relfrozenxid in all tables. If a table approaches the XID wraparound threshold, trigger an automatic VACUUM FREEZE on that table.
[0028] The regular review and performance tuning step periodically review the vacuum logs and database performance to ensure the smart vacuum system is functioning as expected. Also, the priority rules are adjusted, scheduling intervals and vacuum cost settings based on system performance and workload changes.
[0029] The multi-phase transaction management step includes various factors. The XID Usage Minimization helps to assign a new XID to each phase, ensuring no single transaction holds an XID for too long. The consistency and integrity help to track dependencies between phases to ensure that partial commits are consistent with the overall transaction. The dependency tracking helps to ensure that data written in one phase is available and consistent for subsequent phases. The rollback and recovery help to handle failures gracefully by allowing rollback of either individual phases or the entire multi-phase transaction. A long-running transaction can be logically split into phases. Each phase represents a distinct subset of the transaction’s operations, such as reading or querying a subset of data, processing or modifying data in manageable chunks (e.g., batch updates), writing or committing changes to disk. For example, if a transaction performs large batch inserts, the transaction can be split into smaller insert operations, each handled in a separate phase.
[0030] Each phase may depend on data produced or modified in a previous phase. The track dependencies between phases step tracks these dependencies to ensure the correctness of the transaction. The dependency tracking mechanism store metadata (such as intermediate results or phase outputs) that indicates which rows were processed in each phase. Also, this uses a dependency log to record the completion of each phase and which data or operations are dependent on earlier phases. The create a transaction manager module is responsible for splitting the long transaction into phases, assigning each phase a new XID, tracking dependencies between phases, and handling partial commits and ensuring consistency across phases. The transaction manager is responsible for beginning a new phase by starting a new transaction with a unique XID, commit each phase upon completion and update the dependency log, and rollback an individual phase without affecting previously committed phases if necessary.
[0031] The phase commit and rollback mechanism step help to commit the phase to release the XID after each phase completes, minimizing XID retention and allows VACUUM to reclaim tuples earlier. Also, if a phase fails rollback is performed only to that phase without affecting other committed phases. The rollbacks ensure data consistency and allow the transaction to retry or abort gracefully.
[0032] The phase-level isolation step is used to isolate each phase from other transactions (but not from other phases of the same transaction) to ensure that changes in one phase do not interfere with others. This uses standard postgreSQL isolation levels (e.g., READ COMMITTED or REPEATABLE READ) to manage visibility of data between phases. Also, the handling visibility method helps phase 2 to have visibility of data committed by phase 1 if phase 1 inserts data that phase 2 needs to read. Also, a combination of explicit transaction control and dependency management can be used to ensure that each phase has access to the data it needs from earlier phases.
[0033] The long-running queries in phases step is responsible for read-heavy phases, breaking up large queries into smaller parts or using cursor-based fetching. This helps avoid holding locks or XIDs for extended periods.
[0034] The optimize phase execution for large transactions step is responsible for large, long-running transactions (e.g., bulk inserts, updates), breaking the work into batches. The batch insertions or updates are divided into manageable chunks to limit the size and duration of each phase. For example, if you need to insert 1 million rows, the work can be broken into 10 phases, each inserting 100,000 rows.
[0035] The phase scheduling and resource management step makes use of intelligent scheduling to execute phases during low database load periods to minimize contention for system resources (CPU, I/O, memory). Also, a mechanism to pause or delay the execution of phases is implemented based on system resource usage.
[0036] The automatic retry of failed phases step automatically attempt a retry either of the failed phase or the entire transaction in case of a failure during any phase. Also, this implements error handling and recovery procedures that allow rollback of specific phases, or the entire transaction based on the severity of the failure.
[0037] The audit and monitoring step helps to implement a detailed logging of all phases, their start and end times, and any failures or retries. Also, this is responsible for monitoring the transaction and phase status using tools like Prometheus or pg_stat_activity to track long-running transactions and their impact on XID consumption. The multi-phase transaction system validates that XID usage is minimized, and that the database remains consistent across phases under various conditions, including long-running transactions with high concurrency, failure scenarios to ensure proper rollback and retry mechanisms, performance impact of phase-based execution on overall transaction throughput. Also, this helps to tune the phase sizes and the number of operations performed in each phase to balance between reducing XID retention and minimizing overhead from transaction commits.
[0038] The design goals for the XID recycling and reclamation engine involve XID recycling, XID reclamation, minimize XID retention, safety and consistency. The XID recycling helps to identify safe opportunities to reuse XIDs from short-lived transactions that have already been committed and whose tuples are no longer referenced. The XID reclamation helps to ensure that old XIDs are "frozen" when appropriate, preventing the system from holding onto unnecessary XID values. The minimize XID retention helps to reduce the number of XIDs that are held onto by long-running transactions or tables that see frequent updates. The safety and consistency help to ensure that recycling does not violate the database's ACID properties and that all transactions remain consistent.
[0039] The monitor and track transaction lifecycles step helps to track each transaction’s lifecycle to determine whether it is a candidate for XID recycling. This uses the postgreSQL system catalog, such as pg_stat_activity, and the visibility map in pg_class to monitor the state of all transactions. Also, this focuses on short-lived transactions that are frequently committed or aborted as these are ideal candidates for XID recycling since their data is no longer needed after commit/abort.
[0040] The safe reclamation conditions step checks for condition that XIDs can be recycled only if the transaction is fully committed or aborted, all tuples related to the transaction are either marked as visible (committed) or removed (for aborted transactions), and there are no ongoing transactions that depend on the tuples modified by the old XIDs. Also, this determines whether an XID is safe to reclaim by checking the xmin (minimum XID of a table’s tuples) and xmax (maximum XID) values of the tuples. The XIDs can be reclaimed if the tuples are no longer needed for active transactions. Also, this is responsible for regularly monitoring the age of each XID to detect candidates for freezing or recycling and set a threshold (e.g., when the XID age exceeds 1 billion) to trigger reclamation processes before XID wraparound becomes a risk. A recycling strategy is used to focus on short-lived transactions and small tables where transactions complete quickly and do not hold onto XIDs for long durations, utilize the visibility map and transaction logs to mark tuples that can be safely frozen, use a background worker to recycle XIDs periodically, and ensuring that the engine runs alongside the autovacuum process but also focuses on minimizing XID consumption. The recycling mechanism is used for short-lived transactions to identify tuples where the XIDs can be safely marked as "frozen," allowing those XIDs to be reused in the future and to develop a system to maintain a pool of recycled XIDs that can be reassigned to new transactions. The auto extend XID reclamation engine helps to detect if a table’s tuples are safe to be frozen based on their visibility status. Also this is responsible in freezing XIDs earlier, especially for short-lived transactions, to reclaim more XIDs for reuse by implementing parallel vacuuming to speed up the process of reclaiming XIDs by freezing tuples across multiple workers. The tables that frequently experience short-lived transactions (e.g., log tables, session tables) can generate a large number of XIDs quickly. This can be used to focus XID recycling efforts on such tables with high transaction turnover by optimizing the recycling engine to target the tables for XID reclamation and freezing. The maintenance of the XID reclamation engine uses postgreSQL’s autovacuum framework or a custom background worker to run the XID recycling process at regular intervals and ensure that the reclamation engine checks for tables nearing the XID wraparound threshold and proactively freezes old XIDs before the threshold is reached.
[0041] The edge cases and long-running transactions step checks to detect long-running transactions and delay recycling until they are completed if there are long-running transactions that hold onto XIDs for extended periods. Also, this splits long-running transactions into phases (as discussed in multi-phase transaction management) to allow earlier XID release. And also keeps track of which XIDs have been recycled, which tuples have been frozen, how many XIDs have been reclaimed in each cycle. Also, this ensures that the system is successfully reclaiming XIDs and reducing the XID consumption rate.
[0042] The scale XID recycling based on workload step scale the XID recycling engine as transaction volumes increase to reclaim more XIDs proactively. Also, this dynamically adjust vacuum and freeze thresholds based on workload and the age of the oldest active XID.
[0043] The Smart Vacuum Process helps to enhance the vacuum process with context-aware scheduling and prioritization. The smart vacuum system automatically prioritizes vacuuming tables that are nearing the age limit or that are critical to maintaining database integrity. This also helps to distribute vacuum operations more evenly over time to avoid performance bottlenecks.
[0044] The automatic multi-phase transaction management system is used for long-running transactions that risk holding XIDs for extended periods, implement an automatic multi-phase transaction management system. Also, this system would allow long transactions to be split into phases, with each phase using a different XID and can track dependencies between phases to ensure consistency while minimizing the XID usage footprint.
[0045] The XID recycling and reclamation engine is responsible for intelligently reclaiming XIDs from completed transactions in a way that minimizes the risk of wraparound. This engine also identifies safe opportunities to recycle XIDs, particularly in tables where transactions are short-lived and well-contained.
[0046] The real-time XID monitoring dashboard is integrated to provide DBAs with visibility into XID usage, vacuum status, and predicted wraparound risks. The dashboard offers actionable insights and recommendations, such as triggering an emergency vacuum or adjusting transaction management settings.
[0047] The adaptive XID space management helps to implement adaptive XID space management that dynamically adjusts the threshold for triggering autovacuum based on system load, transaction volume, and other contextual factors. This ensures that the autovacuum process runs efficiently, without unnecessary overhead, while effectively preventing wraparound.
[0048] The automatic wraparound mitigation system proactively manages and reorganizes XID (transaction ID) allocation by automatically triggering a global reorganization of XID ranges when the system detects that the overall XID space in a PostgreSQL database is approaching wraparound. The system may dynamically redistribute XIDs across different database contexts, prioritize critical transactions, and manage non-essential operations to prevent the XID counter from wrapping around, thereby ensuring continuous operation without manual intervention. The system uses XID space monitoring and threshold detection, global XID reorganization, transaction prioritization, context-aware XID redistribution, deferred and batched transactions, and emergency vacuum and cleanup triggers to facilitate reorganization of XID ranges.
[0049] The XID space monitoring and threshold detection method continuously monitor the global XID counter and track XID usage across different contexts (e.g., tables, schemas, sessions). The method sets thresholds that trigger the mitigation process as the XID counter approaches its wraparound limit (e.g., within 100 million transactions of the maximum XID).
[0050] The global XID reorganization method automatically initiates a global reorganization of XID ranges upon detecting that the XID space is nearing wraparound. This involves redistributing unused or underutilized XID ranges from less critical contexts to those that are essential or experiencing higher transaction volumes. The reorganization ensures that critical operations continue without disruption.
[0051] The transaction prioritization method is implemented as a prioritization mechanism that classifies transactions based on their importance and urgency. Critical transactions, such as those required for core business functions, are given priority access to the remaining XID space. Non-essential transactions, such as background maintenance tasks or low-priority user queries, may be delayed or throttled to conserve XIDs.
[0052] The context-aware XID redistribution method dynamically reallocates XID ranges based on real-time context. For example, if a particular schema or table is generating a high volume of transactions, the system can temporarily allocate a larger XID range to that context while scaling back on less active contexts. This adaptive approach helps to manage the remaining XID space more effectively.
[0053] The deferred and batched transactions method is used for non-critical operations that can be deferred, the system batches transactions together and assigns them a single XID range when the system load decreases. This reduces the immediate consumption of XIDs during high-demand periods and helps prolong the available XID space.
[0054] The emergency vacuum and cleanup triggers method automatically trigger an emergency vacuum process if the XID space is critically low despite reorganization and prioritization effort. This method may target the most XID-heavy tables or contexts, aggressively reclaiming XIDs by removing dead tuples and completing transactions that are holding onto XIDs unnecessarily.
[0055] Also, the tool/ system provides additional features such as dynamic query prediction and caching, ensuring high-performance query execution without explicit user intervention, dynamic AI powered schema revolution, dynamic data compression in postgreSQL, and quantum query optimization and execution in postgreSQL. The dynamic query prediction and caching, ensuring high-performance query execution without explicit user intervention system has an objective to predict which queries are likely to be run in the future based on patterns, then cache their results to speed up execution times without manual optimization. This enables detailed query logging to capture all incoming SQL queries and helps to gather metrics about frequently executed queries, execution time, and query patterns. This also periodically collect query statistics to build a dataset of frequently executed queries and their associated parameters and stores query logs and usage statistics in a dedicated table for future training data. This uses features like query frequency, time of day, users, or data access patterns to train a machine learning model that predicts the likelihood of query recurrence. And this also uses common algorithms like Decision Trees, Random Forests, or Recurrent Neural Networks (RNNs) for sequence-based prediction. The model is trained with the dataset of query patterns to recognize repeat queries and trends and the model is deployed to run periodically, analysing query logs to predict the most probable next queries.
[0056] A caching layer using in-memory data stores is implemented. The results of queries predicted by the model are cached ahead of time. The result set is stored in the cache when the predicted query is executed. Using preemptive query execution, the layer periodically triggers the pre-execution of predicted queries, storing the results in the cache. When the user executes one of these queries, the database system returns the cached result immediately instead of processing the query again. Also using determine cache expiry an appropriate expiration time is set for cached results. Expiration time can be based on the nature of the data (e.g., cache data with a time-to-live of 5 minutes for frequently changing data, or longer for static data). Also invalidating cache ensure that cache invalidation happens when the underlying data changes (for example, an INSERT, UPDATE, or DELETE operation). This is done using database triggers to invalidate or refresh the cache when modifications occur. The routing predicted queries helps to modify the application or middleware to check the cache before hitting the database for frequently executed queries. This can be done using a query caching proxy layer. The automate query retrieval from cache helps to automatically retrieve and return the result from the cache without intervention if a query is executed and it matches one in the cache. The system should seamlessly return cached data without the end user noticing any difference in the behaviour. The fallback to database falls back to normal query execution but cache the result after this execution for future use in case a predicted query is not cached. The measure cache effectiveness helps to track how often cached results are used versus direct database execution. Also, this collects statistics on query execution time, cache hit ratio, and overall database performance improvement. The refine prediction model uses real-time feedback on cache hit rates and query patterns to refine your prediction model. The adapt to evolving query patterns helps to continuously retrain the prediction model to adapt to changing query patterns and workloads. The query-specific caching is responsible for certain types of queries (e.g., complex analytical queries), and use specific caching rules to prioritize these for prediction and caching. The balance cache size and memory usage are responsible to dynamically adjust the cache size based on system resources and query workload to optimize performance without exhausting memory.
[0057] The dynamic and AI enhanced indexing system has various methods. The data collection mechanism helps to enable detailed query and usage logging. The query logs capture all SQL queries executed, including their execution plans and performance metrics. The system metrics monitor CPU usage, memory consumption, disk I/O, and network latency during query execution. The user behaviour tracks user interactions, session durations, and access times to identify patterns. The aggregate historical data is used to store logs in a centralized data warehouse or analytics platform.
[0058] The perform advanced data analysis and feature extraction involve methods like analyze query patterns, extract features for machine learning, dimensionality reduction. The analyze query pattern involves identifying the most commonly executed queries and their execution frequency, and also detecting queries with long execution times or high resource consumption. The extract features for machine learning are based on query features including number of joins, subqueries, filters, aggregation functions, the data distribution including statistics about data skewness, cardinality, and null value distribution, and the temporal patterns including time-of-day or day-of-week trends in query execution. The dimensionality reduction is used to apply techniques like principal component analysis (PCA) to reduce feature space and eliminate redundant features.
[0059] The develop AI models tailored for indexing helps to select appropriate AI techniques like reinforcement learning, graph neural networks, meta-learning. The reinforcement learning (RL) helps to model indexing as a Markov Decision Process where the agent (AI) learns optimal indexing strategies through interactions with the environment (database). The graph neural networks (GNNs) represent the database schema and queries as graphs to capture relational information. The meta-learning helps to implement models that can learn from a small amount of data and adapt quickly to new patterns. Also, this helps to define the objective function based on performance metrics and cost functions. The performance matrix is used to minimize query execution time, resource utilization, or maximize throughput. The cost functions include penalties for index maintenance overhead and storage costs. Accordingly, the models are trained using historical data to train the AI models and implement cross-validation and hyperparameter tuning to optimize model performance.
[0060] The implement a dynamic indexing engine helps in automated index creation by developing scripts or using APIs to create indexes based on AI model recommendations. Also, conditional indexing is used to create partial indexes for specific query conditions to optimize storage and performance. Automated index removal helps to identify and drop unused or redundant indexes to free up resources, and using decay functions to lower the priority of indexes that haven't been utilized recently. Index configuration management stores index definitions and changes in a version-controlled repository. Also, this helps to implement automated rollback mechanisms in case of performance degradation.
[0061] The integrate AI models with the database system has embedded AI components that use database extensions or stored procedures to embed AI functionalities directly within the DBMS. This functions in PL/Python or PL/Java to call AI models. And has external AI services to host AI models as microservices that communicate with the database via APIs.
[0062] The enhance the query optimizer with AI insights uses optimizer hints and directives to allow the AI model to provide hints to the query optimizer about which indexes to use. Also, this helps to modify the optimizer to accept external recommendations. The cost model adjustment helps to incorporate AI-predicted costs into the optimizer's cost model to improve plan selection and adjust weighting factors based on AI insights. The adaptive query execution plans enable the database to switch execution plans on-the-fly if the AI model predicts better alternatives.
[0063] The ensure real-time adaptability uses online learning algorithms to use algorithms capable of updating the model incrementally as new data arrives. The examples include online gradient descent or incremental decision trees. Also sliding window analysis helps to focus on recent data to adapt to changing workloads and adjust the window size based on the volatility of query patterns. The threshold-based triggers define thresholds for performance metrics that, when crossed, prompt immediate model re-evaluation or index adjustments.
[0064] The implement advanced index structures include learned index structures that replace traditional index data structures with AI models that predict data positions. Also, this implements models like recursive model indexes (RMIs) for sorted data. And hybrid indexes help to combine multiple indexing techniques (e.g., B-tree and hash indexes) based on data characteristics and uses AI to determine the optimal mix for different data segments. The adaptive data layouts allow the AI to reorganize data storage formats (e.g., row-store vs. column-store) based on query patterns.
[0065] The address scalability and performance challenges include parallel processing, caching mechanism, and load balancing. The parallel processing helps to distribute AI computations across multiple CPUs or GPUs to handle large datasets. And uses distributed computing frameworks like Apache Spark or Dask. The caching mechanisms implements intelligent caching informed by AI predictions to reduce disk I/O and uses in-memory databases or data grids for frequently accessed data. The load balancing helps to distribute queries and indexing tasks evenly across database clusters and uses AI to predict peak times and adjust resource allocation proactively.
[0066] The ensure robustness and reliability deals with anomaly detection, fault tolerance, security measures. The anomaly detection implements AI models that detect unusual patterns or anomalies in query behaviour, triggering alerts or automatic interventions when anomalies are detected. The fault tolerance helps to design the system to handle failures gracefully, with failover mechanisms for both the database and AI components and uses redundant systems and data replication. The security measures encrypt sensitive data used by the AI models and implements authentication and authorization for AI components interacting with the database.
[0067] The develop user interfaces and control panels provides visualization dashboards, administrative controls, explainability features. The visualization dashboards provide graphical representations of indexing performance, AI model accuracy, and system health, and uses tools like Grafana or custom web applications. The administrative controls allow DBAs to override AI decisions or adjust parameters manually and helps to implement approval workflows for critical indexing changes. The explainability features offer explanations for AI recommendations to build trust and facilitate debugging also helps to display feature importances or decision paths.
[0068] The establish governance and compliance protocols utilizes policy definition, regulatory compliance, and audit and reporting. The policy definition defines policies for what the AI system is allowed to modify, and boundaries are set for index creation (e.g., maximum index size, types of indexes). The regulatory compliance helps to ensure that data handling by the AI components complies with regulations like GDPR, HIPAA, etc, and implements data anonymization techniques where necessary. The audit and reporting help to keep detailed logs of all AI actions and indexing changes and generate regular reports for compliance audits.
[0069] The plan for continuous improvement uses regular model evaluation, feedback mechanism, stay updated with AI advances. The regular model evaluation helps to schedule periodic assessments of AI model performance and use new data to retrain models and prevent model drift. The feedback mechanisms are responsible to collect feedback from end-users and DBAs on system performance, incorporating human expertise to refine AI strategies. The stay updated with AI advances helps to monitor research in AI and database technologies and attend industry conferences and participate in professional communities.
[0070] The consider ethical implications accounts for fairness, transparency, accountability. Fairness ensures that the AI system does not inadvertently prioritize certain queries or users over others. Also, this implements fairness constraints in the AI models. Transparency is to be open about the use of AI in database indexing and this provides documentation and user education on how the system operates. Accountability assigns responsibility for AI decisions to specific roles or teams and has protocols in place for addressing issues arising from AI actions.
[0071] The implement advanced monitoring and alerting systems uses real-time analytics, custom alert conditions, integration with devops tools. The real-time analytics makes use of stream processing platforms like Apache Flink to analyze data in real-time and helps to detect performance issues as they occur. The custom alert conditions help to define complex conditions that trigger alerts, such as combinations of high CPU usage and slow query times. This uses AI to predict potential future issues and alert proactively. The integration with DevOps tools helps to connect monitoring systems with tools like pagerDuty or opsgenie for incident management, and automate responses to certain alerts, such as rolling back recent index changes.
[0072] The optimize resource utilization makes use of dynamic resource allocation, and energy efficiency. The dynamic resource allocation helps to adjust database resources like memory allocation and thread pools based on AI predictions. This uses container orchestration platforms to scale resources up or down. The energy efficiency implements AI models that optimize for lower energy consumption, especially in large data centers and schedules resource-intensive tasks during off-peak energy pricing periods.
[0073] The explore collaborative filtering techniques has community learning, and federated learning. The Community Learning helps to anonymize and aggregate data across multiple databases or clients to improve AI models if applicable. And uses collaborative filtering to recommend indexes based on similar usage patterns. The federated learning is responsible to train AI models across multiple decentralized devices or servers while keeping data localized, thereby enhancing privacy and compliance by not transferring raw data.
[0074] The incorporate natural language processing (NLP) use query understanding, and user feedback analysis. The query understanding makes use of NLP to parse and understand complex queries better also it improves feature extraction for AI models by understanding query semantics. The user feedback analysis helps to analyze user feedback or support tickets to identify performance issues not captured by logs and use sentiment analysis to gauge user satisfaction.
[0075] The dynamic AI powered schema revolution system involves automation, impact assessment, and continuous learning. The automation helps to automatically suggest schema changes when new features are introduced. The impact assessment ensures that every schema change is analyzed for its impact on performance, data consistency, and existing queries. And the continuous learning helps to continuously improve recommendations based on past decisions and feedback. The AI module manages evolving application requirements by suggesting or applying schema changes. Feature Change Detection helps to integrate the AI system with the application development pipeline (e.g., using Git, CI/CD systems) to detect when new features or requirements are introduced. This uses natural language processing (NLP) to analyze developer comments, API changes, or design documents that describe new data requirements. Database query analysis monitors database queries to detect patterns in usage that suggest new or changing data needs (e.g., new query types, changes in query frequency, or complex joins), and this uses AI-based query parsing to understand how queries interact with the schema and identify performance bottlenecks. The schema usage monitoring helps to monitor how tables, columns, and indexes are being used (e.g., which fields are frequently queried, rarely updated, or underutilized) as well as track query execution times, index usage, and locking behaviour to understand schema performance under evolving workloads.
[0076] Build the AI-Powered Schema Recommender uses data collection for AI model training, model training, and embedding schema structures. The data collection for AI model training collects a historical log of schema changes, along with performance metrics before and after changes were applied and helps to capture data on query performance, data integrity issues, downtime, and user feedback to build a dataset for training the AI model. The model training uses supervised learning with past schema change data, labelled as successful or problematic based on performance and data integrity impacts. Also, this trains models to understand which schema changes (e.g., adding columns, modifying indexes, restructuring tables) lead to optimal performance and employ reinforcement learning where the AI learns from decisions made during schema revolution and refines its recommendations. The embedding schema structures use graph-based AI models (e.g., Graph Neural Networks) to represent schema structures, capturing relationships between tables, columns, indexes, and constraints. Also, this represents queries as graph embeddings to understand how queries interact with the schema, these embeddings can help the AI understand the impact of schema changes.
[0077] The automate schema change suggestions require AI-driven suggestions, and schema impact simulation. The AI-driven suggestions system generates schema change suggestions, such as adding new columns, optimizing indexes, or splitting tables for better normalization when new features or requirements are detected. The AI system also provides detailed justifications for each suggested change, including how the change supports the new feature and its expected impact on performance. The schema impact simulation simulates the impact of schema modifications in a testing environment before applying changes. Also, this is responsible for the AI-based simulations to evaluate the impact on query performance, transaction throughput, and locking behaviour and analyze dependencies between tables and application logic to assess the risk of introducing breaking changes.
[0078] The optimize for performance and scalability involves index optimization, partitioning and sharding, and columnar and row-oriented storage recommendations. The index optimization is performed based on query usage patterns. The AI recommends index modifications (e.g., adding, removing, or reorganizing indexes) to optimize read/write performance and uses machine learning algorithms like decision trees or genetic algorithms to explore index combinations that maximize performance while minimizing storage and maintenance overhead. The partitioning and sharding suggests table partitioning strategies (e.g., horizontal or vertical partitioning) or sharding to improve scalability for high-volume datasets by making use of unsupervised learning models like clustering algorithms to group related data that could be split into separate partitions or shards. The columnar and row-oriented storage recommendations suggest whether certain tables or columns would benefit from columnar storage for analytics-heavy workloads or row-oriented storage for transactional workloads based on usage patterns and uses reinforcement learning to determine the best storage formats based on evolving query patterns.
[0079] The ensure data integrity and compliance makes use of automatic integrity check, schema validation against business rules, and data migration planning. The automatic integrity check ensures that all schema changes are automatically validated for data integrity (e.g., foreign key relationships, unique constraints), and use AI models to predict potential integrity issues based on historical schema changes and corresponding integrity violations. The schema validation against business rules helps to validate schema changes against pre-defined business rules, ensuring that new features do not violate existing constraints or compliance requirements. This also helps to integrate rule-based AI to enforce organizational policies (e.g., GDPR compliance, data retention rules) on the schema. The data migration planning automatically plan and recommend the safest way to migrate data with minimal disruption when schema changes require data migration (e.g., column renames, type changes), and makes use of machine learning to predict potential migration risks (e.g., data loss, downtime) based on past migrations and suggest mitigation strategies.
[0080] The apply safe schema changes in a controlled manner utilizes AI-assisted versioning and rollback, automated schema change testing, and live schema modification. The AI-assisted versioning and rollbacks helps the AI system to manage schema versioning, ensuring that every schema change is tracked and can be rolled back if necessary. Also, this uses predictive models to assess the likelihood of needing a rollback based on past schema changes and current application behaviour. The automated schema change testing runs automated tests that include query performance benchmarks, transaction validation, and data integrity checks before applying changes. This uses AI-based regression testing to ensure that schema changes do not negatively affect existing queries or application logic. The live schema modification suggests strategies for minimizing downtime during schema changes, such as online schema migration, locking minimization, or rolling updates. for live production databases. This uses reinforcement learning to determine the best time to apply schema changes based on real-time system load and user behaviour.
[0081] The continuous monitoring and learning use real-time performance monitoring, feedback loop for continuous learning, and self-tuning capabilities. The real-time performance monitoring helps to continuously monitor query performance and resource usage after schema changes are applied and uses AI-based anomaly detection to identify performance degradations or other unintended consequences of the schema changes. The feedback loop for continuous learning incorporates feedback from developers and database administrators to refine the AI’s recommendations and uses feedback on false positives (bad recommendations) and false negatives (missed optimizations) to improve model accuracy. The self-tuning capabilities allow the AI system to continuously learn from its own actions and optimize future schema recommendations. This also apply meta-learning techniques to enable the AI to adjust its learning algorithms dynamically as the schema evolves over time.
[0082] The ensure collaboration between AI and human experts makes use of huma-in-the-loop feedback, and explainable AI for schema changes. The human-in-the-loop feedback helps to incorporate human feedback loops where database administrators (DBAs) or developers review and approve AI-suggested changes before they are applied. This also provides interfaces for developers to tweak AI recommendations, allowing for fine-grained control over schema modifications. The explainable AI for schema changes ensures that the AI system is transparent, offering explanations for every schema recommendation and use explainability tools like SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations) to provide clear reasoning behind the AI’s decisions, such as why certain columns should be indexed or why certain tables should be partitioned.
[0083] The expand AI capabilities with contextual awareness utilizes contextual awareness of application behaviour, and historical context and trend analysis. The contextual awareness of application behaviour helps to build contextual understanding into the AI, allowing it to consider broader application changes (e.g., new API endpoints, changes in application logic) when recommending schema modifications. This uses knowledge graphs to capture relationships between application components and the database schema, enabling deeper insights into how schema changes will affect overall system behaviour. The historical context and trend analysis helps to analyze past schema changes, usage trends, and system performance to predict future schema requirements. This uses time-series forecasting models to anticipate when certain parts of the schema will need to scale or be optimized for new workloads.
[0084] The implement a scalable deployment architecture has modular architecture. The AI system can be deployed as a modular service, scalable to different environments (e.g., on-premise, cloud databases). This uses microservices-based architectures, where the AI can be invoked via API calls, integrating with CI/CD pipelines, database management systems, and monitoring tools.
[0085] The regular evaluation and model improvement has performance benchmarks to regularly evaluate the effectiveness.
[0086] The dynamic data compression in PostgreSQL system helps to optimize storage efficiency by minimizing the storage footprint of the database and by applying intelligent, data-specific compression. The system dynamically chooses compression algorithms based on data types, storage media (e.g., SSD, HDD, cloud storage), and access patterns, and optimize space usage without negatively affecting query speed. Also, this adapts to performance metrics like I/O throughput, CPU usage, and response times based on data access patterns. The system uses data type identification, wherein the AI system start by identifying the types of data stored in the database (e.g., numeric, text, JSON, binary, geospatial) and classifies the data by its compressibility potential (e.g., repetitive text fields are highly compressible, while random numeric data may be less so). The access patterns and query workload help to monitor query frequency, access methods (sequential vs. random), and read/write ratios, also uses query logs and database metrics to identify hot (frequently accessed) vs. cold (rarely accessed) data. The system analyzes historical data usage trends to predict future access patterns. The storage media characteristics helps to determine the underlying storage type (e.g., SSDs, HDDs, cloud storage) and its read/write performance characteristics, also helps to identify storage I/O bandwidth, latency, and resource constraints to tailor compression strategies that align with the storage performance. The postgreSQL usage metrics is responsible to monitor postgreSQL-specific metrics such as disk usage per table, index sizes, buffer hit ratios, and I/O performance. This also uses capture statistics from postgreSQL’s pg_stat_statements and pg_stat_user_tables to track table-specific performance and query behaviour.
[0087] The build a data compression recommendation engine uses data collection for model training, AI model for compression strategy selection, feature engineering, and adaption with reinforcement learning. The data collection for model training collects data on compression techniques used in postgreSQL compression for large objects and measure their impact on performance. This also helps to build a dataset of PostgreSQL operations, including compression performance (compression ratio, query latency) across different data types and workloads. The AI model for compression strategy selection helps to train machine learning models using supervised learning with historical data about compression ratios, query performance, and storage efficiency and uses a variety of algorithms, such as decision trees or neural networks, to predict the best compression technique based on data type and usage patterns. The feature engineering is responsible to extract features such as query frequency, data type, index size, table size, write patterns, storage latency, and data access modes to help the model make informed decisions. This includes hardware characteristics (e.g., CPU type, memory availability, storage bandwidth) as additional features. The adaptation with reinforcement learning implements reinforcement learning (RL), wherein the AI system learns by applying different compression strategies in a controlled environment. This also allows the RL agent to optimize its policy by maximizing a reward function based on a trade-off between storage savings and query performance.
[0088] The implement dynamic compression strategies make use of AI- driven compression technique selection, adaptive compression tuning, and hybrid compression strategy. The AI-driven compression technique selection is based on the model’s predictions, dynamically apply compression algorithms, such as LZ4, Zlib, Snappy, or custom algorithms based on data type, storage, and access patterns. Also, for text-heavy data, this recommends algorithms like LZ4 or Zstd for fast compression, while for numerical data, uses delta encoding or run-length encodisng to reduce storage size. The adaptive compression tuning helps to continuously adjust compression levels (e.g., light vs. aggressive compression) based on evolving data usage patterns. Here, for read-heavy tables, light compression is applied to maintain fast query performance, while for write-heavy or archived tables, more aggressive compression is applied for maximum storage savings. The hybrid compression strategies is responsible for databases with mixed workloads (OLTP and OLAP), this suggests hybrid compression where different tables or even table partitions use different compression algorithms. Also, this uses clustering or segmentation algorithms to group tables with similar characteristics and apply specialized compression per group.
[0089] The automate compression workflow integration utilizes schema-level compression suggestions, real-time compression adjustments, and compression pipeline integration. The schema-level compression suggestions integrate the AI system into the schema design process to suggest compression strategies when new tables are created. The AI can recommend time-based partitioning combined with compression to optimize both query performance and storage for large datasets (e.g., time-series data, logs). The real-time compression adjustments is responsible for live databases to implement the ability to apply compression changes in real time without disrupting ongoing operations (e.g., using PostgreSQL’s background processes).This uses non-blocking compression adjustments for PostgreSQL’s TOAST mechanism for large objects, minimizing downtime. The compression pipeline integration helps to build the AI system as a microservice or plugin for PostgreSQL that automatically applies compression optimizations in the background. Also this helps to integrate with PostgreSQL’s VACUUM process to automatically trigger compression adjustments during routine database maintenance.
[0090] The optimize compression for hardware and storage type uses compression based on storage media, CPU- aware compression, and multi-tier storage optimization. The compression based on storage media uses AI to select compression strategies that are optimized for different storage types. For instance, on HDDs, favour more aggressive compression to reduce I/O latency and minimize physical reads, and on SSDs, balance compression levels to reduce storage usage without overwhelming the fast I/O bandwidth. The CPU-aware compression uses AI systems that considers the available CPU resources before applying CPU-intensive compression techniques. And for CPU-constrained environments, the AI recommends lightweight compression algorithms like Snappy that optimize for speed rather than maximum compression. The multi-tier storage optimization is used for systems with multi-tier storage (e.g., caching layers, cold storage), the AI recommends different compression strategies per tier such as, in-memory caching and uses minimal compression for speed, and cold archival storage wherein high-compression techniques are applied for data that is infrequently accessed.
[0091] The implement learning-based compression refinement uses continuous monitoring and feedback loop, and online learning for dynamic environments. The continuous monitoring and feedback loop helps to continuously monitor compression performance through query execution times, compression/decompression costs, and storage metrics. This makes use of feedback loops where the system learns from ongoing compression performance, adjusting future decisions based on past outcomes. The online learning for dynamic environments implements online learning techniques to allow the model to evolve as data usage patterns and hardware resources change. This uses incremental learning algorithms, wherein the AI system updates its compression strategy in real time as new data is ingested or as storage constraints change.
[0092] The ensure data integrity and performance consistency uses query performance validation, and safe rollback mechanism. The query performance validation facilitates to validate the impact of each compression strategy on query performance using AI-based simulations and benchmarking tools. Also, this compares compression performance against historical baselines to ensure that new compression strategies do not degrade query speeds. The safe rollback mechanism implements a rollback mechanism that allows the AI system to revert to previous compression states if performance degrades. This uses rollback triggers where if the AI detects a negative impact (e.g., query slowdowns), it automatically restores the previous compression settings.
[0093] The adapt compression to data lifecycle uses cold vs. hot data differentiation, and compression over time. The cold vs. hot data differentiation helps the AI system to classify data into cold (infrequently accessed) and hot (frequently accessed) categories based on access patterns. This applies more aggressive compression to cold data while uses lighter compression for hot, frequently accessed tables. The compression over time is used for time-series or archival data, the AI recommends applying stronger compression to older, less frequently accessed data. This uses temporal-based compression algorithms where data that ages out is automatically compressed more aggressively without human intervention.
[0094] The implement data-driven compression monitoring tools uses visualization dashboards, and alert and recommendation. The visualization dashboards helps to build user-friendly dashboards that allow administrators to monitor compression performance metrics, storage savings, and query latency. This provides insights into how compression strategies are impacting database operations, including compression ratios and performance improvements. The alerts and recommendations facilitate an alerting system that notifies administrators when compression needs adjustment (e.g., when query performance drops or storage thresholds are reached). This helps the AI to provide proactive recommendations, such as compressing underutilized tables or optimizing high-query tables with less aggressive compression.
[0095] The ensure scalability of compression system makes use of horizontal scaling for large database, and compression on partitioned data. The horizontal scaling for large datasets uses AI-powered compression systems that can scale horizontally, especially for databases with large datasets or distributed databases. Here, the AI intelligently distributes compression tasks across nodes in a distributed PostgreSQL system for efficient parallel processing. The compression on partitioned data is responsible for partitioned tables, the AI recommends different compression strategies for each partition based on data access frequency or age. This uses partition-aware compression where each partition has its own compression settings, and older partitions are automatically compressed more aggressively.
[0096] The periodic evaluation and model improvement utilizes continuous improvement, user feedback integration, and quantum- enhanced query optimization. The continuous improvement helps to periodically re-train the AI models using new data on compression efficiency, query performance, and system resources. Also, the models are adapted to new storage technologies. The user feedback integration incorporates DBA and developer feedback into the AI system, allowing users to fine-tune compression strategies based on real-world experience. This uses active learning, wherein the AI system asks for human input when uncertain about the best compression technique, continuously improving its knowledge. The PostgreSQL uses a quantum co-processor to optimize queries by finding the most efficient execution path, the quantum-enhanced query optimization system implements quantum algorithms (like Grover’s search) to explore all possible query execution plans in parallel, selecting the optimal one. The quantum-assisted optimizer reduces the search space drastically as classical query optimizers face exponential complexity when dealing with large datasets or complex joins.
[0097] The quantum query optimization and execution in PostgreSQL system employs quantum computing to perform complex query optimization in PostgreSQL. Quantum algorithms, such as grover's search and quantum annealing, are used to solve NP-hard problems related to query execution plans, index optimization, and data clustering at exponentially faster rates compared to classical methods. This hybrid classical-quantum system is used for query optimization and data management in PostgreSQL that offloads complex computational tasks such as multi-join optimization, clustering, indexing, and predictive caching to a quantum processor. Also, this system provides superior performance in query execution and database maintenance by leveraging quantum computing principles, ensuring exponential speed-up for large-scale, complex database queries.
[0098] The quantum-assisted data clustering for indexing makes use of quantum algorithms like quantum annealing and performs data clustering and partitioning for indexing optimization in PostgreSQL. The quantum module would analyze data access patterns and automatically recommend the best partitioning and indexing strategies, this ensures faster query responses for large-scale data without the need for manual tuning.
[0099] The quantum-based predictive caching and resource allocation uses quantum algorithms to predict future query loads and pre-cache data or allocate system resources efficiently. The quantum module simulates multiple potential future states of the database and user interactions in parallel, allowing PostgreSQL to adjust its cache and memory allocation in real-time, reducing latency for high-traffic queries.
[00100] The quantum secure data encryption in transit and at rest uses the quantum key distribution (QKD) methods to ensure secure transmission of data between PostgreSQL instances and clients. The quantum encryption algorithms help to protect data stored in the database from classical and quantum cyberattacks, ensuring that sensitive information remains safe in both classical and quantum computing environments.
[00101] The quantum-assisted multi-join optimization is used for highly complex queries involving multiple joins across vast datasets, the quantum module evaluates all possible join orders simultaneously using quantum superposition, selecting the join order with the least computational cost. This dramatically reduces the time needed for complex analytics and reporting queries, which would typically be resource intensive.
[00102] The system includes various components. The quantum co-processor integration component uses a specialized quantum co-processor connected to the PostgreSQL engine that would be responsible for running quantum algorithms. The PostgreSQL instance would offload certain optimization and analytical tasks to the quantum co-processor, which returns results for immediate use. The classical-quantum hybrid query planner makes use of a hybrid query planner that uses classical computing for basic tasks and offloads quantum-appropriate tasks (e.g., complex optimizations, data partitioning) to the quantum module. The planner includes a decision engine that determines when to invoke the quantum processor based on the complexity of the query and available resources. The quantum-oriented data structures component develops special data structures and algorithms to interface between PostgreSQL and the quantum module, allowing efficient data transfer and processing. These data structures support quantum states and operations, enabling fast data retrieval and query execution on the quantum processor.
[00103] The system has various uses cases like big data analytics, IoT data management, high-frequency trading system, and AI and machine learning data pipelines. The big data analytics is used for large-scale analytics where massive datasets and complex query plans require processing, and quantum-enhanced optimization drastically reduces computation time. The IoT data management helps PostgreSQL to manage IoT data streams by utilizing quantum-assisted predictive algorithms for real-time analytics and resource allocation, and ensuring efficient data processing. The high-frequency trading systems facilitate quantum-enhanced query execution and optimization in PostgreSQL, providing real-time insights for high-frequency trading platforms, where rapid decision-making and minimal latency are crucial. The AI and machine learning data pipelines helps the quantum-assisted PostgreSQL to process and optimize large training datasets more efficiently, reducing model training times when integrated into AI/ML pipelines.
[00104] Referring to FIG. 1, an exemplary illustration of an environment 100 is disclosed. The environment 100 includes a system 105, a remote client device 110, and remote transaction devices 115 in communication with each other via a network 120. The system 105 corresponds to, includes, and/or implements the enhanced database management and optimization system 125 described above in the present disclosure. The system 105 also includes storage unit 130 for storing/hosting a query-based transaction database including, but not limited to, a PostgreSQL database. The storage unit 130 may also store different artificial intelligence (AI)/machine learning (ML) models. The enhanced database management and optimization engine 125 may be configured to monitor and implement the various AI/ML models described herein to perform the various steps or implement the different modules, features, and/or systems described in the present disclosure corresponding to the query-based transaction database. The enhanced database management and optimization engine 125 may be configured to monitor the query-based transaction database by monitoring various database transactions initiated by the remote transaction devices 115. The remote client device 110 may be configured to provide analytics related to the various steps, modules, features, and/or systems implemented by the enhanced database management and optimization engine 125. The remote client device 110 may be configured to provide instructions to the system 105 to perform or implement one or more additional steps, modules, features, and/or systems associated with the various steps, modules, features, and/or systems of the enhanced database management and optimization system 125.
[00105] Referring to FIG. 2, a schematic block diagram of a system 105 for database management is disclosed. Examples of the system 105 may include, but are not limited to, a server, a database server, a virtual server, or a cloud based sever provided by a database server provider. The system 105 may be configured for relational database management system and may store, retrieve, and manage data for applications, users, clients, corporations, etc.
[00106] In an embodiment, the system 105 includes a bus 200 or other communication mechanism for communicating information, and a processor 210 coupled with the bus 200 for processing information. The system 105 also include a memory 215, such as a random-access memory (RAM) or other dynamic storage device, coupled to the bus 200 for storing information and instructions to be executed by the processor 210. The memory 215 can be used for storing temporary variables or other intermediate information during execution of instructions to be executed by the processor 210. The system 105 further includes a read only memory (ROM) 220 or other static storage device coupled to the bus 200 for storing static information and instructions for processor 210. A storage unit 225, such as a magnetic disk, solid state drive, optical disk, etc., is provided and coupled to the bus 200. The storage unit 225 may be configured as a database to store data. The storage unit 225 may be provided inside the system 105 or outside the system 105. In some embodiments, the storage unit 225 may be fragmented into multiple units, where one or more units are provided inside the system 105 and the remaining units may be configured outside the system 105 and may be coupled to the system 105 via the bus 200 or a communication interface 245 of the system 105. Further, in some embodiments, the storage unit 225 may function as a distributed database, in-memory database, or federated database and may include one or more volumes. The processor 210 may perform read and write operations on the storage unit 225 and may execute instructions to manage or optimize the database configured in the storage unit 225.
[00107] The system 105 can be coupled via the bus 200 to an output unit 230. The output unit 230 may include a display unit, an audio unit, or both. The display unit may be a cathode ray tube (CRT), liquid crystal display (LCD), or Light Emitting Diode (LED) display for displaying information to the user. Further, the audio unit may be a speaker, such as single channel speaker or multi-channel speaker for providing an audio output to the user. The processor 210 is configured to generate a visual output for displaying information of the display unit or audio output for producing audio signals from the audio unit. An input device 235, including alphanumeric and other keys, is coupled to bus 200 for communicating information and command selections to the processor 210. Another type of user input device is a cursor control 240, such as a mouse, a trackball, a trackpad, a touch pad, or cursor direction keys for communicating direction information and command selections to the processor 210 and for controlling cursor movement on the display unit. The input devices 235 or 240 can also be included in the display, for example a touch screen and may also include compatible devices with the display unit such as a stylus.
[00108] Various embodiments are related to the use of system 105 for implementing the techniques described herein. In one embodiment, the techniques are performed by the system 105 in response to the processor 210 executing instructions included in the memory 115. Such instructions can be read into the memory 215 from another machine-readable medium. Execution of the instructions included in the memory 215 causes the processor 210 to perform the process steps described herein.
[00109] The term “machine-readable medium” as used herein refers to any medium that participates in providing data that causes a machine to operate in a specific fashion. In an embodiment implemented using the system 105, various machine-readable medium is involved, for example, in providing instructions to the processor 210 for execution. The machine-readable medium can be a storage media. Storage media includes both non-volatile media and volatile media. Non-volatile media includes, for example, optical or magnetic disks, such as storage unit 125. Volatile media includes dynamic memory, such as the memory 115. All such media must be tangible to enable the instructions carried by the media to be detected by a physical mechanism that reads the instructions into a machine.
[00110] Common forms of machine-readable medium include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, or any other magnetic medium, a CD-ROM, any other optical medium, punch cards, paper-tape, any other physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, any other memory chip or cartridge.
[00111] In another embodiment, the machine-readable medium can be a transmission media including coaxial cables, copper wire and fiber optics, including the wires that comprise the bus 200. Transmission media can also take the form of acoustic or light waves, such as those generated during radio-wave and infra-red data communications. Examples of machine-readable medium may include but are not limited to a carrier wave as described hereinafter or any other medium from which the system 105 can read, for example online software, download links, installation links, and online links. For example, the instructions can initially be carried on a magnetic disk of a remote computer. The remote computer can load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to the system 105 can receive the data on the telephone line and use an infra-red transmitter to convert the data to an infra-red signal. An infra-red detector can receive the data carried in the infra-red signal and appropriate circuitry can place the data on the bus 200. The bus 200 carries the data to the memory 215, from which the processor 210 retrieves and executes the instructions. The instructions received by the memory 215 can optionally be stored on storage unit 125 either before or after execution by the processor 210. All such media must be tangible to enable the instructions carried by the media to be detected by a physical mechanism that reads the instructions into a machine.
[00112] In an embodiment, the system 105 may be coupled to a network. The network may include communication networks such as, but not limited to, a Local Area Network (LAN), a Wireless Local Area Network (WLAN), a Wide Area Network (WAN), internet, a Small Area Network (SAN), and the Internet. The system 105 includes the communication interface 245 coupled to the bus 200. The communication interface 245 provides a two-way data communication coupling to the network. For example, the communication interface 245 can be an integrated service digital network (ISDN) card or a modem to provide a data communication connection to a corresponding type of telephone line. As another example, the communication interface 245 can be a local area network (LAN) card to provide a data communication connection to a compatible LAN. Wireless links can also be implemented. In any such implementation, the communication interface 245 sends and receives electrical, electromagnetic, or optical signals that carry digital data streams representing various types of information.
[00113] The processor 210 may include the enhanced database management and optimization engine 125. The enhanced database management and optimization engine 125 may implement one or more AI models trained to optimize performance of at least one database configured at the system 105. The processor 210 is configured to monitor at least one performance metric of the at least one database in real-time based on one or more on-going transactions associated with the at least one database. The at least one performance metric comprises at least one query performance metric, throughput metric, resource utilization metric, storage metric, uptime metric, error tracking metric, schema metric, or any combination thereof;
[00114] The query Performance metric may include query response time (average, median, 95th percentile), queries per second (QPS), slow query count and execution time, and query execution plans and optimization. The throughput Metric may include transactions per second (TPS), read/write ratio Operations per second, concurrent connections. The resource utilization metric may include processer utilization, percentage memory usage (buffer pools, cache hit ratios), memory allocation, garbage collection, and connection pool usage. The storage metric may include disk I/O operations per second (IOPS), read/write latency, Storage space utilization, index size and fragmentation, The uptime metric describes availability and reliability and may include database availability percentage, mean time between failures (MTBF), mean time to recovery (MTTR), replication lag (for distributed systems). The error tracking metric includes connection failures, timeout errors, deadlock frequency, failed transactions Further, the schema metric corresponds to relational databases and may include lock wait time, table scan frequency, index usage statistics, cache hit ratios (buffer pool, query cache), and parallel processing data.
[00115] The processor 210 may predict, via at least one AI model, in advance at least one anomaly, error, deadlock, resource exhaustion, resource over-utilization, or any combination thereof expected to occur corresponding to the at least one database based on the at least one monitored performance metric, historical data associated with the at least one database, or any combination thereof. Thereafter, the processor 210 identifies at least one preemptive action to be performed corresponding to the at least one predicted anomaly, error, deadlock, resource exhaustion, or resource over-utilization, or any combination thereof expected to occur, wherein the at least one identified preemptive action optimizes the at least one monitored performance metric. Accordingly, at least one action based on the at least one identified preemptive action is performed by the processor 210.
[00116] In an example, the processor 210, via the at least one AI model, may determine that current system load for a database has following parameters system_cpu_usage > 80, parallel_workers = max(2, original_workers // 2) if memory_pressure > 70, work_memory = min(work_mem, '128MB'). The processor 210 may refer to historical data where in instance of past optimization indicates the following: historical_performance = ‘query_pattern': 'join_aggregate’, hint_applied': 'HashJoin(a b) Parallel(a 4)', improvement_actual': 0.42, # 42% actual improvement 'improvement_predicted': 0.35 # vs 35% predicted. In other words, for executing the database query ‘join aggregate’, it is identified to perform parallel processing with 4 parallel works with expectation of 35% increase in efficiency. However, the actual improvement in efficiency was 42%. Thus, the processor 210, based on past optimization, may identify that at least one preemptive action is to increase the parallel works from 2 to 4.
[00117] Further, the processor 210 is configured to predict warparound exhaustion. The processor 210 monitors a count of generation of unique identifiers (UIDs) in the database, wherein each UID of the generated UIDs is associated with a transaction data stored in the database. The processor 210, via at least one trained AI model, determines a time period required for the count of the generated UIDs to reach a wraparound threshold based on one or more factors. The one or more factors include the historical data, a current rate of generation of UIDs, a predicted rate of generation of UIDs, or age of UIDs of current transactions. The processor 210 monitors the age of UIDs of current transactions. The at least one AI model predicts the rate of generation of UIDs. In an embodiment, the transaction data includes financial transaction details and wherein the UIDs are transaction identifiers (XIDs).
[00118] When the determined time period is within a predetermined range of a threshold time period, the processor 210 performs at least one action. The at least one action includes vacuuming the database, scaling up the database to accommodate additional UIDs, generating an alert, or any combination thereof.
[00119] The processor 210 displays a UID consumption rate and the determined time period required for the count of the UIDs to reach the wraparound threshold on the display 230.
[00120] Further, the processor 210 is configured to generate a predetermined number of UIDs, one or more sets of UIDs in the predetermined number of UIDs are associated with one or more type of transactions, and each set of UIDs in the one or more sets of UIDs has a corresponding wraparound threshold. The wraparound threshold is the exhaustion of a set of UIDs available for a type of transactions.
[00121] In some embodiments, the UIDs includes a first set of UIDs reserved for high-priority transactions and the processor 210 monitors a request to generate a UID for a current transaction, analyze metadata corresponding to the current transaction based on a predefined requirement, associate a type of transaction from the one or more type of transactions with the current transaction based on the analysis, generate the UID from a set of UID corresponding to the type of transaction associated with the current transaction, and allocate the generated UID to the current transaction for processing.
[00122] In some embodiments, the at least one action performed by the processor 210 may include freezing active tuples in the database and blocking reuse of the generated UIDs corresponding to the frozen tuples.
[00123] Further, the processor 210 generates the historical data by storing transaction statistical information of the database at predetermined intervals and previous predictions along with the performance results of preemptive actions.
[00124] Further, when the processor 210 receives a database query from the remote client device 110, the processor 210 determines resource utilization for executing the query and compares the determined resource utilization with optimal resource utilization. The optimal resource utilization is based on the monitored performance metrics and the historical data. When the determined resource utilization is greater than the optimal resource utilization, the processor 210 may identify following preemptive actions: modification of the query, modification of at least one table corresponding to the query, assigning priority to the query corresponding to priorities assigned to other queries or additional resource allocation. The processor 210 may execute queries according to the priorities assigned to the queries.
[00125] Further, the modification of the at least one table corresponding to the query may include modification of schema of the at least one table or modification of indexing of the at least one table.
[00126] FIG. 3 is a schematic illustration of a method 300 for database management, in accordance with the embodiment of the present disclosure. The method 300 may be implemented by the processor 210. At step 305 at least one performance metric of the at least one database is monitored the processor 210 in real-time based on one or more on-going transactions associated with the at least one database. At step 310, at least one anomaly, error, deadlock, resource exhaustion, resource over-utilization, or any combination thereof expected to occur corresponding to the at least one database is predicted in advance by the processor 210 via at least one AI model. The prediction is based on the at least one monitored performance metric, historical data associated with the at least one database, or any combination thereof. At step 315, at least one preemptive action to be performed corresponding to the at least one predicted anomaly, error, deadlock, resource exhaustion, or resource over-utilization, or any combination thereof expected to occur is identified by the processor 210. The at least one identified preemptive action optimizes the at least one monitored performance metric. At step 320, at least one action based on the at least one identified preemptive action is performed by the processor 210.
[00127] FIG. 4 is a schematic illustration of a method 400 for managing generation of unique identifiers in a database, in accordance with the embodiment of the present disclosure. At step 405, a count of generation of unique identifiers (UIDs) in the database is monitored by the processor 210. Each UID in the generated UIDs is associated with the transaction data stored in the database. At step 410, a time period required for the count of the generated UIDs to reach a wraparound threshold is determined by the processor 210 based on one or more factors. The one or more factors include historical data, a current rate of generation of UIDs, a predicted rate of generation of UIDs, age of UIDs of current transactions, or any combination thereof. At step 415, when the determined time period is within a predetermined rage of a threshold time period, at least one action including vacuuming the database, scaling up the database to accommodate additional UIDs, generating an alert, or any combination thereof is performed by the processor 210.
[00128] FIG. 5 is a schematic illustration of a method 500 for optimizing a database query, in accordance with the embodiment of the present disclosure. At step 505, when a database query is received by the processor, resource utilization for executing the database query is determined by the processor 210 via the at least one AI model. At step 510, the determined resource utilization is compared with optimal resource utilization by the processor 210. The optimal resource utilization is based on the monitored performance metrics and the historical data. At step 515, when the determined resource utilization with optimal resource utilization, an action including any one of modification of the query, modification of at least one table corresponding to the query, or additional resource allocation is performed by the processor 210. Assignment of a priority to the query is with respect to the priorities assigned to other queries and the processor 210 executes queries according to the priorities assigned to the queries.
[00129] In the preceding specification, the present disclosure and its advantages have been described with reference to specific embodiments. However, it will be apparent to a person of ordinary skill in the art that various modifications and changes can be made, without departing from the scope of the present disclosure, as set forth in the claims below. Accordingly, the specification and figures are to be regarded as illustrative examples of the present disclosure, rather than in restrictive sense. All such possible modifications are intended to be included within the scope of present disclosure. ,CLAIMS:1. A system for database management, comprising:
at least one database having one or more volumes of storage;
a processor; and
a memory for storing instructions, that when executed by the processor, causes the processor to:
monitor at least one performance metric of the at least one database in real-time based on one or more on-going transactions associated with the at least one database, wherein the at least one performance metric comprises at least one query performance metric, throughput metric, resource utilization metric, storage metric, uptime metric, error tracking metric, schema metric, or any combination thereof;
predict, via at least one artificial intelligence (AI) model, in advance at least one anomaly, error, deadlock, resource exhaustion, resource over-utilization, or any combination thereof expected to occur corresponding to the at least one database based on the at least one monitored performance metric, historical data associated with the at least one database, or any combination thereof;
identify at least one preemptive action to be performed corresponding to the at least one predicted anomaly, error, deadlock, resource exhaustion, or resource over-utilization, or any combination thereof expected to occur, wherein the at least one identified preemptive action optimizes the at least one monitored performance metric; and
perform at least one action based on the at least one identified preemptive action.

2. The system as claimed in claim 1, wherein the processor is configured to:
monitor a count of generation of unique identifiers (UIDs) in the database, wherein each UID of the generated UIDs is associated with a transaction data stored in the database;
determine, using the at least one AI model, a time period required for the count of the generated UIDs to reach a wraparound threshold based on one or more factors, wherein the one or more factors include the historical data, a current rate of generation of UIDs, a predicted rate of generation of UIDs, or age of UIDs of current transactions; and
perform the at least one action when the determined time period is within a predetermined rage of a threshold time period, wherein the at least one action includes vacuuming the database, scaling up the database to accommodate additional UIDs, generating an alert, or any combination thereof.

3. The system as claimed in claim 2, wherein the processor generates the historical data by storing transaction statistical information of the database at predetermined intervals.

4. The system as claimed in claim 2, wherein the processor monitors the age of UIDs of current transactions.

5. The system as claimed in claim 2, wherein the at least one AI model predicts the rate of generation of UIDs.

6. The system as claimed in claim 2, wherein the transaction data includes financial transaction details and wherein the UIDs are transaction identifiers (XIDs).

7. The system as claimed in claim 2, wherein:
the processor is configured to generate a predetermined number of UIDs,
one or more sets of UIDs in the predetermined number of UIDs are associated with one or more type of transactions, and
each set of UIDs in the one or more sets of UIDs has a corresponding wraparound threshold, wherein the wraparound threshold is the exhaustion of a set of UIDs available for a type of transactions.

8. The system as claimed in claim 7, wherein the UIDs includes a first set of UIDs reserved for high-priority transactions.

9. The system as claimed in claim 7, wherein the processor is configured to:
monitor a request to generate a UID for a current transaction;
analyze metadata corresponding to the current transaction based on a predefined requirement;
associate a type of transaction from the one or more type of transactions with the current transaction based on the analysis;
generate the UID from a set of UID corresponding to the type of transaction associated with the current transaction; and
allocate the generated UID to the current transaction for processing.

10. The system as claimed in claim 1, wherein the at least one action includes:
freezing active tuples in the database and blocking reuse of the generated UIDs corresponding to the frozen tuples.

11. The system as claimed in claim 6, wherein the processor displays a UID consumption rate and the determined time period required for the count of the UIDs to reach the wraparound threshold on a display.

12. The system as claimed in claim 1, wherein, when the processor receives a database query, the processor is configured to:
determine resource utilization for executing the query; and
compare the determined resource utilization with optimal resource utilization, wherein the optimal resource utilization is based on the monitored performance metrics and the historical data; and
wherein, when the determined resource utilization is greater than the optimal resource utilization, the determined at least one preemptive action includes at least one of:
modification of the query;
modification of at least one table corresponding to the query;
assigning priority to the query corresponding to priorities assigned to other queries, wherein the processor executes queries according to the priorities assigned to the queries; or
additional resource allocation.

13. The system as claimed in claim 13, wherein the modification of the at least one table corresponding to the query includes one of:
modification of schema of the at least one table; or
modification of indexing of the at least one table.

14. A method for database management, the method comprising:
monitoring, by a processor implementing at least one artificial intelligence (AI) model, at least one performance metric of the at least one database in real-time based on one or more on-going transactions associated with the at least one database, wherein the at least one performance metric comprises at least one query performance metric, throughput metric, resource utilization metric, storage metric, uptime metric, error tracking metric, schema metric, or any combination thereof;
predicting, by the processor via the at least one AI model, in advance at least one anomaly, error, deadlock, resource exhaustion, resource over-utilization, or any combination thereof expected to occur corresponding to the at least one database based on the at least one monitored performance metric, historical data associated with the at least one database, or any combination thereof;
identifying, by the processor, at least one preemptive action to be performed corresponding to the at least one predicted anomaly, error, deadlock, resource exhaustion, or resource over-utilization, or any combination thereof expected to occur, wherein the at least one identified preemptive action optimizes the at least one monitored performance metric; and
performing, by the processor, at least one action based on the at least one identified preemptive action.

15. The method as claimed in claim 14, wherein the method comprises:
monitoring, by the processor, a count of generation of unique identifiers (UIDs) in the database, wherein each UID in the generated UIDs is associated with a transaction data stored in the database;
determining, by the processor using the at least one AI model, a time period required for the count of the generated UIDs to reach a wraparound threshold based on one or more factors, wherein the one or more factors include historical data, a current rate of generation of UIDs, a predicted rate of generation of UIDs, age of UIDs of current transactions, or any combination thereof; and
performing, by the processor, the at least one action when the determined time period is within a predetermined rage of a threshold time period, wherein the at least one action includes vacuuming the database, scaling up the database to accommodate additional UIDs, generating an alert, or any combination thereof.

16. The method as claimed in claim 14, wherein the method comprises:
determining, by the processor, resource utilization for executing a database query; and
comparing, by the processor, the determined resource utilization with optimal resource utilization, wherein the optimal resource utilization is based on the monitored performance metrics and the historical data; and
wherein, when the determined resource utilization with optimal resource utilization, the determined at least one action includes at least one of:
modification of the query;
modification of at least one table corresponding to the query;
assigning priority to the query corresponding to priorities assigned to other queries, wherein the processor executes queries according to the priorities assigned to the queries; or
additional resource allocation.