Optimize Query Performance for Real-Time Trades
Description: Optimize database queries related to real-time trade execution and order matching to reduce latency and improve transaction speed.
Acceptance Criteria: Queries should return results in under 100ms, even under high load conditions, and be scalable to handle peak trading volumes.
Implement Database Partitioning for Trade History
Description: Implement database partitioning strategies for large datasets like trade history to improve read/write performance and reduce query execution times.
Acceptance Criteria: The database should be partitioned by date or trade ID, improving query performance on historical data by at least 30%.
Database Replication for High Availability
Description: Set up database replication to ensure high availability and failover support in case of server outages or database crashes.
Acceptance Criteria: Data should be replicated across at least two database instances, ensuring that the system can automatically switch to the replica in the event of failure without data loss.
Real-Time Data Sync Across Microservices
Description: Ensure real-time data synchronization across different microservices (e.g., order book, user portfolio, trade execution) using a distributed database architecture.
Acceptance Criteria: Data consistency across microservices should be maintained within a 1-second delay, ensuring that trades and portfolio updates reflect immediately.
Data Backup and Restore Procedures
Description: Develop automated backup and restore procedures for critical trading data, including user portfolios, order books, and trade logs.
Acceptance Criteria: The backup process should occur hourly, with full data restore capabilities tested and verified to ensure that data loss is minimized to within the last hour.
Improve Scalability for High-Volume Trading Days
Description: Implement a scaling solution for database operations during high-volume trading periods, such as market opens or earnings reports.
Acceptance Criteria: The database should handle 2x the normal transaction volume without significant degradation in performance, with automatic scaling in place during peak hours.
Ensure ACID Compliance for Trade Transactions
Description: Ensure full ACID (Atomicity, Consistency, Isolation, Durability) compliance for all trade-related transactions to prevent errors such as incomplete or double trades.
Acceptance Criteria: All trade-related transactions should meet ACID compliance, with proper rollback mechanisms in place for failed trades or order executions.
Create Indexing Strategy for Trading Data
Description: Develop an indexing strategy to improve query performance on key data sets such as user portfolios, order books, and trade histories.
Acceptance Criteria: Indexes should be created on frequently queried fields (e.g., trade timestamp, stock symbol) to reduce query times by at least 40%.
Set Up Data Encryption for User and Trade Data
Description: Implement data encryption for all sensitive user and trade data stored in the database to comply with security regulations and protect against breaches.
Acceptance Criteria: All sensitive data, including personal user information, trade records, and payment details, should be encrypted at rest and in transit using industry-standard encryption protocols.
Audit Trail for Trade Modifications and Cancellations
Description: Build an audit trail system to log and track all modifications, cancellations, or updates made to trades and orders in the platform.
Acceptance Criteria: The audit trail should log all changes with timestamps, user IDs, and action details, and be retrievable for regulatory or compliance purposes within 10 seconds.
These backlog items focus on improving the performance, security, and scalability of the database for a trading platform, ensuring smooth operations even under high-demand conditions.
Optimize Trade History Query Performance
Description: Optimize database queries related to fetching trade history to ensure fast retrieval, even with large datasets for active users.
Acceptance Criteria: Trade history queries should return results within 2 seconds for users with up to 10,000 trades, with indices properly applied on key columns.
Implement Partitioning for Large Transaction Tables
Description: Apply database partitioning on large tables (e.g., trade transactions, order books) to improve query performance and manage large datasets effectively.
Acceptance Criteria: Tables should be partitioned by time or user ID, with a noticeable performance improvement in read and write operations on these tables.
Data Replication for High Availability
Description: Set up database replication across multiple data centers to ensure high availability and fault tolerance in case of server failure.
Acceptance Criteria: Replication should be active and tested, with failover times under 30 seconds during a primary database failure.
Audit Log for Trading Activities
Description: Implement a detailed audit log to track all trading activities, including order placements, modifications, and cancellations, for compliance and security monitoring.
Acceptance Criteria: The audit log should capture user ID, timestamp, activity type, and relevant trade details, and be queryable for up to 6 months of history.
Historical Market Data Storage and Retrieval
Description: Design and implement a database schema to store historical market data (e.g., price ticks, volumes) for efficient retrieval and analysis.
Acceptance Criteria: Historical market data should be available for query, supporting time-based searches with results returned in less than 1 second for common intervals (1-minute, 1-hour).
Improve Write Scalability for Order Book Updates
Description: Optimize the database architecture to handle high-frequency order book updates, ensuring minimal write contention during peak trading times.
Acceptance Criteria: The system should handle up to 1,000 order book updates per second without significant degradation in performance.
Database Schema Refactoring for User Profiles
Description: Refactor the database schema for user profiles to separate frequently updated fields (e.g., balances) from static information (e.g., name, KYC data), improving read/write performance.
Acceptance Criteria: Refactored schema should reduce locking issues on frequently updated fields and improve the performance of user-related queries.
Real-Time Data Sync Between Cache and Database
Description: Implement a real-time data synchronization mechanism between the database and in-memory caches to ensure consistency across the platform.
Acceptance Criteria: Data should be synced within 100ms between the database and cache for critical information like order statuses and account balances.
Data Encryption at Rest
Description: Implement data encryption at rest for sensitive data, such as user account details, personal information, and trading records, to comply with security standards.
Acceptance Criteria: Sensitive data fields should be encrypted in the database, with no noticeable impact on read/write performance.
Backup and Restore Strategy for Trading Data
Description: Develop a robust backup and restore process for all trading data, ensuring data integrity and quick recovery in case of corruption or data loss.
Acceptance Criteria: Full database backups should occur daily, with incremental backups every hour, and the system should be able to restore data within 15 minutes in case of failure.
Real-Time Data Validation
Pre-Fill Common Data Fields
Multi-Language Support for Data Entry
Conditional Fields Based on User Input
Registration Confirmation and Review Screen
Data Entry Shortcuts for Common Inputs
Optimize Query Performance for Real-Time Trades Description: Optimize database queries related to real-time trade execution and order matching to reduce latency and improve transaction speed. Acceptance Criteria: Queries should return results in under 100ms, even under high load conditions, and be scalable to handle peak trading volumes.
Implement Database Partitioning for Trade History Description: Implement database partitioning strategies for large datasets like trade history to improve read/write performance and reduce query execution times. Acceptance Criteria: The database should be partitioned by date or trade ID, improving query performance on historical data by at least 30%.
Database Replication for High Availability Description: Set up database replication to ensure high availability and failover support in case of server outages or database crashes. Acceptance Criteria: Data should be replicated across at least two database instances, ensuring that the system can automatically switch to the replica in the event of failure without data loss.
Real-Time Data Sync Across Microservices Description: Ensure real-time data synchronization across different microservices (e.g., order book, user portfolio, trade execution) using a distributed database architecture. Acceptance Criteria: Data consistency across microservices should be maintained within a 1-second delay, ensuring that trades and portfolio updates reflect immediately.
Data Backup and Restore Procedures Description: Develop automated backup and restore procedures for critical trading data, including user portfolios, order books, and trade logs. Acceptance Criteria: The backup process should occur hourly, with full data restore capabilities tested and verified to ensure that data loss is minimized to within the last hour.
Improve Scalability for High-Volume Trading Days Description: Implement a scaling solution for database operations during high-volume trading periods, such as market opens or earnings reports. Acceptance Criteria: The database should handle 2x the normal transaction volume without significant degradation in performance, with automatic scaling in place during peak hours.
Ensure ACID Compliance for Trade Transactions Description: Ensure full ACID (Atomicity, Consistency, Isolation, Durability) compliance for all trade-related transactions to prevent errors such as incomplete or double trades. Acceptance Criteria: All trade-related transactions should meet ACID compliance, with proper rollback mechanisms in place for failed trades or order executions.
Create Indexing Strategy for Trading Data Description: Develop an indexing strategy to improve query performance on key data sets such as user portfolios, order books, and trade histories. Acceptance Criteria: Indexes should be created on frequently queried fields (e.g., trade timestamp, stock symbol) to reduce query times by at least 40%.
Set Up Data Encryption for User and Trade Data Description: Implement data encryption for all sensitive user and trade data stored in the database to comply with security regulations and protect against breaches. Acceptance Criteria: All sensitive data, including personal user information, trade records, and payment details, should be encrypted at rest and in transit using industry-standard encryption protocols.
Audit Trail for Trade Modifications and Cancellations Description: Build an audit trail system to log and track all modifications, cancellations, or updates made to trades and orders in the platform. Acceptance Criteria: The audit trail should log all changes with timestamps, user IDs, and action details, and be retrievable for regulatory or compliance purposes within 10 seconds. These backlog items focus on improving the performance, security, and scalability of the database for a trading platform, ensuring smooth operations even under high-demand conditions.
Optimize Trade History Query Performance Description: Optimize database queries related to fetching trade history to ensure fast retrieval, even with large datasets for active users. Acceptance Criteria: Trade history queries should return results within 2 seconds for users with up to 10,000 trades, with indices properly applied on key columns.
Implement Partitioning for Large Transaction Tables Description: Apply database partitioning on large tables (e.g., trade transactions, order books) to improve query performance and manage large datasets effectively. Acceptance Criteria: Tables should be partitioned by time or user ID, with a noticeable performance improvement in read and write operations on these tables.
Data Replication for High Availability Description: Set up database replication across multiple data centers to ensure high availability and fault tolerance in case of server failure. Acceptance Criteria: Replication should be active and tested, with failover times under 30 seconds during a primary database failure.
Audit Log for Trading Activities Description: Implement a detailed audit log to track all trading activities, including order placements, modifications, and cancellations, for compliance and security monitoring. Acceptance Criteria: The audit log should capture user ID, timestamp, activity type, and relevant trade details, and be queryable for up to 6 months of history.
Historical Market Data Storage and Retrieval Description: Design and implement a database schema to store historical market data (e.g., price ticks, volumes) for efficient retrieval and analysis. Acceptance Criteria: Historical market data should be available for query, supporting time-based searches with results returned in less than 1 second for common intervals (1-minute, 1-hour).
Improve Write Scalability for Order Book Updates Description: Optimize the database architecture to handle high-frequency order book updates, ensuring minimal write contention during peak trading times. Acceptance Criteria: The system should handle up to 1,000 order book updates per second without significant degradation in performance.
Database Schema Refactoring for User Profiles Description: Refactor the database schema for user profiles to separate frequently updated fields (e.g., balances) from static information (e.g., name, KYC data), improving read/write performance. Acceptance Criteria: Refactored schema should reduce locking issues on frequently updated fields and improve the performance of user-related queries.
Real-Time Data Sync Between Cache and Database Description: Implement a real-time data synchronization mechanism between the database and in-memory caches to ensure consistency across the platform. Acceptance Criteria: Data should be synced within 100ms between the database and cache for critical information like order statuses and account balances.
Data Encryption at Rest Description: Implement data encryption at rest for sensitive data, such as user account details, personal information, and trading records, to comply with security standards. Acceptance Criteria: Sensitive data fields should be encrypted in the database, with no noticeable impact on read/write performance.
Backup and Restore Strategy for Trading Data Description: Develop a robust backup and restore process for all trading data, ensuring data integrity and quick recovery in case of corruption or data loss. Acceptance Criteria: Full database backups should occur daily, with incremental backups every hour, and the system should be able to restore data within 15 minutes in case of failure.