#OLTP

[cherryjams]

[govindhtech]

5th Gen Intel Xeon Scalable Processors Boost SQL Server 2022

5th Gen Intel Xeon Scalable Processors

While speed and scalability have always been essential to databases, contemporary databases also need to serve AI and ML applications at higher performance levels. Real-time decision-making, which is now far more widespread, should be made possible by databases together with increasingly faster searches. Databases and the infrastructure that powers them are usually the first business goals that need to be modernized in order to support analytics. The substantial speed benefits of utilizing 5th Gen Intel Xeon Scalable Processors to run SQL Server 2022 will be demonstrated in this post.

OLTP/OLAP Performance Improvements with 5th gen Intel Xeon Scalable processors

The HammerDB benchmark uses New Orders per minute (NOPM) throughput to quantify OLTP. Figure 1 illustrates performance gains of up to 48.1% NOPM Online Analytical Processing when comparing 5th Gen Intel Xeon processors to 4th Gen Intel Xeon processors, while displays up to 50.6% faster queries.

The enhanced CPU efficiency of the 5th gen Intel Xeon processors, demonstrated by its 83% OLTP and 75% OLAP utilization, is another advantage. When compared to the 5th generation of Intel Xeon processors, the prior generation requires 16% more CPU resources for the OLTP workload and 13% more for the OLAP workload.

The Value of Faster Backups

Faster backups improve uptime, simplify data administration, and enhance security, among other things. Up to 2.72x and 3.42 quicker backups for idle and peak loads, respectively, are possible when running SQL Server 2022 Enterprise Edition on an Intel Xeon Platinum processor when using Intel QAT.

The reason for the longest Intel QAT values for 5th Gen Intel Xeon Scalable Processors is because the Gold version includes less backup cores than the Platinum model, which provides some perspective for the comparisons.

With an emphasis on attaining near-real-time latencies, optimizing query speed, and delivering the full potential of scalable warehouse systems, SQL Server 2022 offers a number of new features. It’s even better when it runs on 5th gen Intel Xeon Processors.

Solution snapshot for SQL Server 2022 running on 4th generation Intel Xeon Scalable CPUs. performance, security, and current data platform that lead the industry.

SQL Server 2022

The performance and dependability of 5th Gen Intel Xeon Scalable Processors, which are well known, can greatly increase your SQL Server 2022 database.

The following tutorial will examine crucial elements and tactics to maximize your setup:

Hardware Points to Consider

Choose a processor: Choose Intel Xeon with many cores and fast clock speeds. Choose models with Intel Turbo Boost and Intel Hyper-Threading Technology for greater performance.

Memory: Have enough RAM for your database size and workload. Sufficient RAM enhances query performance and lowers disk I/O.

Storage: To reduce I/O bottlenecks, choose high-performance storage options like SSDs or fast HDDs with RAID setups.

Modification of Software

Database Design: Make sure your query execution plans, indexes, and database schema are optimized. To guarantee effective data access, evaluate and improve your design on a regular basis.

Configuration Settings: Match your workload and hardware capabilities with the SQL Server 2022 configuration options, such as maximum worker threads, maximum server RAM, and I/O priority.

Query tuning: To find performance bottlenecks and improve queries, use programs like Management Studio or SQL Server Profiler. Think about methods such as parameterization, indexing, and query hints.

Features Exclusive to Intel

Use Intel Turbo Boost Technology to dynamically raise clock speeds for high-demanding tasks.

With Intel Hyper-Threading Technology, you may run many threads on a single core, which improves performance.

Intel QuickAssist Technology (QAT): Enhance database performance by speeding up encryption and compression/decompression operations.

Optimization of Workload

Workload balancing: To prevent resource congestion, divide workloads among several instances or servers.

Partitioning: To improve efficiency and management, split up huge tables into smaller sections.

Indexing: To expedite the retrieval of data, create the proper indexes. Columnstore indexes are a good option for workloads involving analysis.

Observation and Adjustment

Performance monitoring: Track key performance indicators (KPIs) and pinpoint areas for improvement with tools like SQL Server Performance Monitor.

Frequent Tuning: Keep an eye on and adjust your database on a regular basis to accommodate shifting hardware requirements and workloads.

SQL Server 2022 Pricing

SQL Server 2022 cost depends on edition and licensing model. SQL Server 2022 has three main editions:

SQL Server 2022 Standard

Description: For small to medium organizations with minimal database functions for data and application management.

Licensing

Cost per core: ~$3,586.

Server + CAL (Client Access License): ~$931 per server, ~$209 per CAL.

Basic data management, analytics, reporting, integration, and little virtualization.

SQL Server 2022 Enterprise

Designed for large companies with significant workloads, extensive features, and scalability and performance needs.

Licensing

Cost per core: ~$13,748.

High-availability, in-memory performance, business intelligence, machine learning, and infinite virtualization.

SQL Server 2022 Express

Use: Free, lightweight edition for tiny applications, learning, and testing.

License: Free.

Features: Basic capability, 10 GB databases, restricted memory and CPU.

Models for licensing

Per Core: Recommended for big, high-demand situations with processor core-based licensing.

Server + CAL (Client Access License): For smaller environments, each server needs a license and each connecting user/device needs a CAL.

In brief

Faster databases can help firms meet their technical and business objectives because they are the main engines for analytics and transactions. Greater business continuity may result from those databases’ faster backups.

Read more on govindhtech.com

[rexavki]

Clickhouse : OLAP vs OLTP ???

[snowflakemasters]

OLAP and OLTP Difference

In today's data-driven world, businesses rely on robust database systems to store, manage, and analyze vast amounts of information. Among these systems, OLAP (Online Analytical Processing) and OLTP (Online Transactional Processing) play crucial roles, each serving distinct purposes in database management. 

This article aims to uncover the critical variances between OLAP and OLTP, comprehensively understanding their definition, concepts, diverse perspectives, relevant statistics, and real-world examples. By grasping these fundamental differences, organizations can make informed decisions when selecting the most suitable database system to meet their needs. 

So, let's dive into the intriguing world of OLAP and OLTP and explore how they shape the landscape of database management.

What are OLAP and OLTP?

OLAP (Online Analytical Processing):

OLAP systems typically store data in a denormalized format, which means that data is organized into a structure optimized for analysis rather than transactional processing. This denormalized structure allows for faster query performance and supports complex analytical operations across multiple dimensions. OLAP systems often use specialized databases and storage technologies to efficiently manage and query large volumes of data, enabling users to perform sophisticated analysis tasks with ease.

In contrast, OLTP systems typically store data in a normalized format, which means that data is organized into tables with minimal redundancy to ensure data integrity and reduce storage space. Normalization helps optimize the efficiency of transactional operations by minimizing data duplication and improving data consistency. OLTP systems often prioritize fast read and write operations, with a focus on maintaining data integrity and ensuring the accuracy of transactions in real-time.

OLTP (Online Transactional Processing):

OLTP, which stands for Online Transactional Processing, is a database technology primarily focused on real-time transactional operations. It handles day-to-day transactional tasks such as inserting, updating, and deleting records in a database. OLTP systems are commonly used in applications that require immediate and reliable transaction processing, such as e-commerce platforms, banking systems, and airline reservation systems. 

The main characteristics of OLTP systems include high concurrency, low response times, data integrity, and ACID (Atomicity, Consistency, Isolation, Durability) compliance. Unlike OLAP systems optimized for complex analysis, OLTP systems are designed for write-intensive operations, processing numerous small transactions concurrently.

Key Differences between OLAP and OLTP:

Data Processing Approach: OLAP follows a multidimensional data model, employing queries to analyze and aggregate data from various perspectives. On the other hand, OLTP adopts a relational model, emphasizing real-time transaction processing and maintaining data integrity.

Database Structure: OLAP systems typically utilize a star, snowflake, or hybrid schema for optimal analytical performance. Conversely, OLTP systems employ normalized schemas to eliminate redundancy and support efficient transactional operations.

User Interaction: OLAP systems provide a user-friendly interface that enables end-users to interactively navigate and explore data through features like drill-down, slice-and-dice, and pivoting. In contrast, OLTP systems primarily facilitate standard CRUD (Create, Read, Update, Delete) operations, focusing on quick response times for concurrent transactions.

Performance Requirements: OLAP systems prioritize complex queries and aggregations, often dealing with large datasets. Therefore, they require significant processing power, memory, and storage capabilities. On the other hand, OLTP systems prioritize quick and reliable transaction execution, necessitating high throughput and low response times.

Diverse Perspectives: Industry Applications and Examples:

OLAP in Business Intelligence: Many enterprises leverage OLAP to gain actionable insights from their operational data, enabling informed decision-making and strategic planning. Companies like Amazon and Walmart utilize OLAP for sales analysis, inventory management, and demand forecasting.

OLTP in E-commerce: OLTP plays a vital role in e-commerce platforms, facilitating real-time online transactions, inventory management, and secure payment processing. For instance, platforms like eBay and PayPal rely on OLTP systems to handle high volumes of concurrent transactions.

OLAP vs. OLTP in Finance: In the finance sector, OLAP empowers banks and financial institutions to perform in-depth analysis, risk assessment, and portfolio optimization. In contrast, OLTP ensures secure and accurate execution of financial transactions backed by fraud detection mechanisms.

Relevant Statistics and Research Findings:

According to a report by Gartner, the adoption rate of OLAP and OLTP systems has shown a steady increase in recent years. The survey found that.

78% of organizations utilize OLAP systems for complex data analysis.

87% of organizations have implemented OLTP systems for day-to-day transactional processing.

Benefits of Leveraging OLAP and OLTP Systems

A study by the International Data Corporation (IDC) highlighted the benefits organizations could experience by effectively leveraging OLAP and OLTP systems. The findings reveal that organizations that harness the power of these systems can achieve : 

Higher profitability: By utilizing OLAP and OLTP systems, organizations can gain valuable insights from historical data, enabling better decision-making and strategic planning. These, in turn, can lead to improved profitability.

Improved decision-making capabilities: OLAP systems allow users to perform complex analysis, data mining, and trend analysis, providing decision-makers with accurate and timely information. On the other hand, OLTP systems provide real-time transactional processing, enabling immediate and reliable execution of critical business transactions.

Case Studies: Successful Implementations of OLAP and OLTP:

Case Study 1: Company XYZ Improves Decision-Making with OLAP:

Company XYZ, a multinational retail corporation, implemented an OLAP system to analyze sales data across various dimensions. They gained deep insights into customer behaviour, product performance, and market trends using OLAP's drill-down and slice-and-dice capabilities. That empowered the company to make data-driven decisions, leading to optimized inventory management, targeted marketing campaigns, and increased sales revenue.

Case Study 2: E-commerce Platform Boosts Customer Satisfaction with OLTP:

An e-commerce platform faced challenges handling a high volume of transactions, resulting in slow response times and customer dissatisfaction. Implementing a robust OLTP system improved performance, reducing transaction processing time by 50%. As a result, customers experienced seamless purchasing experiences that led to increased customer satisfaction and repeat business.

Advantages and Limitations of OLAP and OLTP

Advantages of OLAP:

Powerful data analysis capabilities

Flexibility in exploring data from multiple perspectives

Support for complex queries and aggregations

Decision-making support through insights and patterns

Limitations of OLAP:

High resource requirements (processing power, memory, storage)

Longer response times for complex queries

Limited real-time data availability

Advantages of OLTP:

Efficient transaction processing

Data integrity and consistency

High concurrency support

Real-time data availability

Limitations of OLTP:

Limited analytical capabilities

Difficulty handling complex queries and aggregations

Higher maintenance overhead for data consistency

Benefits of OLAP vs OLTP

OLAP and OLTP systems offer distinct benefits for organizations based on their specific needs and use cases.

Computational automation: OLAP systems allow for automated processing of complex data structure computations, reducing the need for manual calculations.

Data mining: OLAP systems can extract valuable insights and patterns from large datasets.

Trend analysis: OLAP systems enable organizations to analyze historical data trends and make informed decisions based on past patterns and behaviours.

Real-time transaction processing: OLTP systems excel at processing real-time or near real-time transactions, allowing immediate updates and smooth customer interactions.

Efficient handling of large data volumes: OLTP systems are designed to handle high volumes of data efficiently, making them ideal for transactional processing in industries such as retail and finance.

Consistency and data integrity: OLTP systems prioritize maintaining data consistency and integrity, ensuring that transactions are accurately recorded and maintained.

It is vital for organizations to carefully evaluate their specific business requirements, data analysis needs, performance considerations, and scalability requirements to determine the most suitable system for their operations. In some cases, a combination of OLAP and OLTP systems may be ideal, as they serve different purposes and can complement each other to meet various organizational needs.

OLTP vs OLAP examples

Here are some examples to illustrate the differences between OLTP and OLAP:

E-commerce Platform: An e-commerce website that allows customers to search for products, add items to their cart, and complete purchases is an example of an OLTP system. It processes numerous small transactions in real-time, such as order placement, inventory updates, and payment processing.

Banking System: A banking system that handles daily transactions like deposits, withdrawals, transfers, and balance inquiries is another example of an OLTP system. It ensures the integrity and consistency of financial data across multiple accounts and processes transactions in real-time.

Business Intelligence Reporting: An organization using an OLAP system to generate complex reports and perform data analysis for decision-making purposes exemplifies an OLAP use case. These reports may involve aggregating large volumes of historical sales data, performing trend analysis, and identifying patterns or correlations.

Data Mining and Analytics: A retailer analyzing customer buying patterns, product sales across regions, and customer segmentation using an OLAP system is another example of OLAP usage. That involves querying and analyzing large volumes of data from multiple dimensions to gain insights and make data-driven decisions.

These examples demonstrate how OLTP and OLAP systems serve different purposes in real-world applications, with OLTP handling real-time transactional tasks and OLAP enabling advanced data analysis and reporting.

Factors to Consider in Choosing between OLAP and OLTP:When deciding between OLAP and OLTP systems, organizations should consider several factors. These factors include:

Nature of the business: It's essential to understand the heart of the company and the type of data that will be processed. That includes the volume, complexity, and type of data the system will handle.

Data analysis requirements: Organizations should also consider the type of analysis required, whether it's simple transactional processing or complex data mining and trend analysis.

Performance needs: Performance is a critical factor to consider based on the size of the data that needs to be processed, as this significantly impacts the processing speed of the system.

Scalability: Organizations should consider if the system is scalable and can accommodate future needs as a business grows.

It's essential to assess the specific goals and objectives when deciding between OLAP and OLTP systems. While an organization might require OLAP systems for complex data analysis, it might also need OLTP systems for day-to-day transactional processing. Therefore, combining both methods may be an ideal solution for meeting different needs. Careful consideration of these factors can lead to selecting the right system that suits an organization's needs, leading to optimal utilization of resources and increased efficiency.

Conclusion:

In conclusion, understanding the critical variances between OLAP and OLTP is essential for organizations seeking to leverage database systems effectively. Whether making strategic decisions based on historical data or processing real-time transactions, selecting the appropriate system can significantly impact a company's success. 

By considering diverse perspectives, analyzing relevant statistics, and exploring real-world case studies, businesses can confidently choose between OLAP and OLTP to maximize the value of their data.

[tekinvaderz]

In today’s data-driven world, effective decision-making relies on different types of data processing to manage and analyze information efficiently. Batch processing is ideal for handling large data sets periodically, while real-time processing provides instant insights for critical decisions. OLTP ensures smooth transaction management, and OLAP allows businesses to uncover trends through multidimensional analysis. Emerging methods like augmented analytics and stream processing leverage AI to automate insights and handle continuous data flows, empowering organizations to stay ahead in a fast-paced environment.

[thanhthienphuvn]

Hướng dẫn download và cài đặt phần mềm SQL Server 2014

Hướng dẫn download và cài đặt phần mềm SQL Server 2014 SQL Server là một hệ quản trị cơ sở dữ liệu (“Relational Database Management System”, viết tắt:

RDBMS) được phát triển bởi Microsoft.

Phần mềm này hỗ trợ người dùng quản lý, phân tích, và khai thác cơ sở dữ liệu một cách hiệu quả.

SQL Server thường được sử dụng hiện tại là phiên bản 2014 có khả năng đạp ứng nhu cầu quản lý dữ liệu cho nhiều doanh nghiệp, từ nhỏ đến lớn, nhờ vào hệ thống tính năng mạnh mẽ, đồng thời hỗ trợ nền tảng Windows.

SQL Server 2014 là một hệ quản trị cơ sở dữ liệu mạnh mẽ, đáp ứng tốt nhu cầu lưu trữ, quản lý và xử lý dữ liệu trong thời đại công nghệ hiện nay.

Với những tính năng nổi bật như In-Memory OLTP, AlwaysOn Availability Groups, và khả năng phân tích dữ liệu tiên tiến, phần mềm này đã trở thành một công cụ không thể thiếu cho các doanh nghiệp và tổ chức.

[ai-news]

#AI #ML #Automation

[helicalinsight]

Seamlessly MySQL to Redshift Migration with Ask On Data

MySQL to Redshift migration is a critical component for businesses looking to scale their data infrastructure. As organizations grow, they often need to transition from traditional relational databases like MySQL to more powerful cloud data warehouses like Amazon Redshift to handle larger datasets, improve performance, and enable real-time analytics. The migration process can be complex, but with the right tools, it becomes much more manageable. Ask On Data is a tool designed to streamline the data wrangling and migration process, helping businesses move from MySQL to Redshift effortlessly.

Why Migrate from MySQL to Redshift?

MySQL, a widely-used relational database management system (RDBMS), is excellent for managing structured data, especially for small to medium-sized applications. However, as the volume of data increases, MySQL can struggle with performance and scalability. This is where Amazon Redshift, a fully managed cloud-based data warehouse, comes into play. Redshift offers powerful query performance, massive scalability, and robust integration with other AWS services.

Redshift is built specifically for analytics, and it supports parallel processing, which enables faster query execution on large datasets. The transition from MySQL to Redshift allows businesses to run complex queries, gain insights from large volumes of data, and perform advanced analytics without compromising performance.

The Migration Process: Challenges and Solutions

Migrating from MySQL to Redshift is not a one-click operation. It requires careful planning, data transformation, and validation. Some of the primary challenges include:

Data Compatibility: MySQL and Redshift have different data models and structures. MySQL is an OLTP (Online Transaction Processing) system optimized for transactional queries, while Redshift is an OLAP (Online Analytical Processing) system optimized for read-heavy, analytical queries. The differences in how data is stored, indexed, and accessed must be addressed during migration.

Data Transformation: MySQL’s schema may need to be restructured to fit Redshift’s columnar storage format. Data types and table structures may also need adjustments, as Redshift uses specific data types optimized for analytical workloads.

Data Volume: Moving large volumes of data from MySQL to Redshift can take time and resources. A well-thought-out migration strategy is essential to minimize downtime and ensure the integrity of the data.

Testing and Validation: Post-migration, it is crucial to test and validate the data to ensure everything is accurately transferred, and the queries in Redshift return the expected results.

How Ask On Data Eases the Migration Process

Ask On Data is a powerful tool designed to assist with data wrangling and migration tasks. The tool simplifies the complex process of transitioning from MySQL to Redshift by offering several key features:

Data Preparation and Wrangling: Before migration, data often needs cleaning and transformation. Ask On Data makes it easy to prepare your data by handling missing values, eliminating duplicates, and ensuring consistency across datasets. It also provides automated data profiling to ensure data quality before migration.

Schema Mapping and Transformation: Ask On Data supports schema mapping, helping you seamlessly convert MySQL schemas into Redshift-compatible structures. The tool automatically maps data types, handles column transformations, and generates the necessary scripts to create tables in Redshift.

Efficient Data Loading: Ask On Data simplifies the process of transferring large volumes of data from MySQL to Redshift. With support for bulk data loading and parallel processing, the tool ensures that the migration happens swiftly with minimal impact on production systems.

Error Handling and Monitoring: Migration can be prone to errors, especially when dealing with large datasets. Ask On Data offers built-in error handling and monitoring features to track the progress of the migration and troubleshoot any issues that arise.

Post-Migration Validation: Once the migration is complete, Ask On Data helps validate the data by comparing the original data in MySQL with the migrated data in Redshift. It ensures that data integrity is maintained and that all queries return accurate results.

Conclusion

Migrating from MySQL to Redshift can significantly improve the performance and scalability of your data infrastructure. While the migration process can be complex, tools like Ask On Data can simplify it by automating many of the steps involved. From data wrangling to schema transformation and data validation, Ask On Data provides a comprehensive solution for seamless migration. By leveraging this tool, businesses can focus on analyzing their data, rather than getting bogged down in the technicalities of migration, ensuring a smooth and efficient transition to Redshift.

[work-at-home-mania]

Net Developer

We are ideally looking for someone that has experience with Online Transaction Processing (OLTP) web application development and batch processing experience. Need someone with state, government, etc type experience. If they have another D…

[verside]

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Important Data Processing System (OLTP vs OLAP)

Not all databases are the same, there are different types of databases for specific workloads, let's understand two of them.

📍 OLTP (Online Transactional Processing):

Processing large volumes of small, individual transactions in real-time, such as bank transactions

📍 OLAP (Online Analytical Processing):

Analyze large volumes of data to support BI such as forecasting

Almost all OLTP systems are row-based, and all of the data are stored row by row.

So when you query any data, it will pull the entire row, even if you just select one column.

So pulling one column = scanning the entire row and then selecting the column

Not efficient!

I made the same mistake early in my career, I ran an Analytics query to get sum/avg on millions of rows

The database server size was tiny and took everything down.

OLTP Examples: MySQL, PostgreSQL, Oracle

On the other hand, the OLAP system is mainly column-based.

So instead of pulling all of the columns, it will only pull columns that are required for analysis.

Specially designed for analysis work.

OLAP Examples: BigQuery, Redshift, Snowflake

[govindhtech]

Prefab Cloud Spanner And PostgreSQL: Flexible And Affordable

Prefab’s Cloud Spanner with PostgreSQL: Adaptable, dependable, and reasonably priced for any size

PostgreSQL is a fantastic OLTP database that can serve the same purposes as Redis for real-time access, MongoDB for schema flexibility, and Elastic for data that doesn’t cleanly fit into tables or SQL. It’s like having a Swiss Army knife in the world of databases. PostgreSQL manages everything with elegance, whether you need it for analytics queries or JSON storage. Its transaction integrity is likewise flawless.

NoSQL databases, such as HBase, Cassandra, and DynamoDB, are at the other end of the database spectrum. Unlike PostgreSQL’s adaptability, these databases are notoriously difficult to set up, comprehend, and work with. However, their unlimited scalability compensates for their inflexibility. NoSQL databases are the giants of web-scale databases because they can handle enormous amounts of data and rapid read/write performance.

However, is there a database that can offer both amazing scale and versatility?

It might have it both ways after its experience with Spanner.

Why use the PostgreSQL interface from Spanner?

At Prefab, Google uses dynamic logging, feature flags, and secrets management to help developers ship apps more quickly. To construct essential features, including evaluation charts, that aid in it operations, scaling, and product improvement, it employ Cloud Spanner as a data store for its customers’ setups, feature flags, and generated client telemetry.

The following are some of the main features that attracted to Spanner:

99.99% uptime by default (multi-availability zone); if you operate in many regions, you can reach up to 99.999% uptime.

Robust ACID transactions

Scaling horizontally, even for writes

Clients, queries, and schemas in PostgreSQL

To put it another way, Spanner offers the ease of use and portability that make PostgreSQL so alluring, along with the robustness and uptime of a massively replicated database on the scale of Google.

How Spanner is used in Prefab

Because Prefab’s architecture is divided into two sections, it made perfect sense for us to have a separate database for each section. This allowed us to select the most appropriate technology for the task. The two aspects of its architecture are as follows:

Using Google’s software development kits (SDKs), developers can leverage its core Prefab APIs to serve their clients.

Google Cloud clients utilize a web application to monitor and manage their app settings.

In addition to providing incredibly low latency, Google’s feature flag services must be scalable to satisfy the needs of the developers’ downstream clients. With Spanner’s support, Java and the Java virtual machine (JVM) are the ideal options for this high throughput, low latency, and high scalability sector. Although it has a much lower throughput, the user interface (UI) of its program must still enable us to provide features to its clients quickly. It uses PostgreSQL, React, and Ruby on Rails for this section of its architecture.

Spanner in operation

The backend for Google Cloud’s dynamic logging’s volume tracking is one functionality that currently makes use of Cloud Spanner. Its SDK transmits the volume for each log level and logger to Spanner after detecting log requests in its customers’ apps. Then, using the Prefab UI, Google Cloud leverages this information to assist users in determining how many log statements will be output to their log aggregator if they enable logging at different settings.

It need a table with the following shape in order to enable this capture:

CREATE TABLE logger_rollup ( id varchar(36) NOT NULL, start_at timestamptz NOT NULL, end_at timestamptz NOT NULL, project_id bigint NOT NULL, project_env_id bigint NOT NULL, logger_name text NOT NULL, trace_count bigint NOT NULL, debug_count bigint NOT NULL, info_count bigint NOT NULL, warn_count bigint NOT NULL, error_count bigint NOT NULL, fatal_count bigint NOT NULL, created_at spanner.commit_timestamp, client_id bigint, api_key_id bigint, PRIMARY KEY (project_env_id, logger_name, id) );

As clients provide the telemetry for Google Cloud’s dynamic logging, this table scales really quickly and erratically. Yes, a time series database or some clever windowing and data removal techniques might potentially be used for this. However, for the sake of this post, this is a simple method to show how Spanner aids in performance management for a table with a large amount of data.

Get 100X storage with no downtime for ⅓ of the cos

It must duplicate Prefab’s database among several zones during production. Because feature flags and dynamic configuration systems are single points of failure by design, reliability is crucial.

Here, Google adopts a belt and suspenders strategy, but its “belt” is robust with Spanner’s uptime SLA and multi-availability zone replication. You would need to treble the cost of a single instance of PostgreSQL to accomplish this. However, replication and automatic failover are included in Cloud Spanner pricing right out of the box. Additionally, you only pay for the bytes you use, and each node has a ton of storage space up to 10TB with Spanner’s latest improvements. This gives the comparison the following appearance for:

The best practice of having a database instance for each environment can become exorbitantly costly at small scales. This was a problem when I initially looked into Spanner a few years back because the least instance size was 1,000 PUs, or one node. Spanner’s scale has since been modified to scale down to less than a whole node, which makes our selection much simpler. Additionally, it allows us to scale up anytime we need to without having to restructure our apps or deal with outages.

Recent enhancements to the Google Cloud ecosystem with Spanner

When we first started using the PostgreSQL interface for Spanner, we encountered several difficulties. Nonetheless, we are thrilled that the majority of the first issues we ran into have been resolved because Google Cloud is always developing and enhancing its goods and services.

Here are a few of our favorite updates:

Query editor: Having a query editor in the Google Cloud console is quite handy as it enables us to examine and optimize any queries that perform poorly.

Key Visualizer: Understanding row keys becomes crucial when examining large-volume NoSQL databases with HBase. It can identify typical problems that lead to hotspots and examine Cloud Spanner data access trends over time with the Key Visualizer.

In brief

Although it has extensive prior experience with HBase and PostgreSQL, it is quite with its choice to use Spanner as Prefab’s preferred horizontally scalable operational database. For its requirements, it has found it to be simple to use, offering all the same scaling capabilities as HBase without the hassles of developing it yourself. It saves time and money when there are fewer possible points of failure and fewer items to manage.

Consider broadening your horizons if you’re afraid of large tables but haven’t explored options other than PostgreSQL. Spanner’s PostgreSQL interface combines the dependable and scalable nature of Cloud Spanner and Google Cloud with the portability and user-friendliness of PostgreSQL.

Start Now

Spanner is available for free for the first ninety days or for as low as $65 a month after that. Additionally, it would be delighted to establish a connection with you and would appreciate it if you could learn more about its Feature Flags, Dynamic Logging, and Secret Management, which are components of the solution built on top of Cloud Spanner.

Read more on Govindhtech.com

[sap001]

What are the differences between SAP HANA and SAP MDG?

SAP HANA (High-Performance Analytic Appliance) and SAP MDG (Master Data Governance) serve different purposes within the SAP ecosystem.

SAP HANA is an in-memory database that allows real-time data processing and analytics. It is primarily used for speeding up transactions and analytics by storing data in-memory rather than on disk, which enhances performance. HANA can support OLAP and OLTP systems, enabling real-time analysis on large datasets.

SAP MDG, on the other hand, focuses on governance, ensuring consistency, and maintaining the quality of master data across an organization. MDG integrates with SAP and non-SAP systems to manage data centrally, ensuring that master data, such as customer or supplier information, is accurate and compliant with business rules.

For those looking to upgrade their SAP skills, Anubhav's online training is highly recommended. Recognized globally, he offers comprehensive courses in SAP, including corporate training. Check out his upcoming courses at Anubhav Trainings.

[sandeep2363]

Intelligent Query Processing in SQL Server: Detailed Overview with Examples

Intelligent Query Processing (IQP) is a feature set introduced in SQL Server 2017 and extended in SQL Server 2019 and SQL Server 2022 to enhance query performance. It automatically adapts and optimizes the execution of queries based on runtime conditions, making it highly valuable for improving performance without needing code changes. IQP helps improve both OLTP (Online Transaction Processing)…

[easyinplay]

从数据库发展历程到数据结构设计探析

一 数据库发展史

起初，数据的管理方式是文档系统，数据存储在文档中，数据管理和维护都由进程员完成。后来发展出树形结构和网状结构的数据库，但都存在着难以扩展和维护的问题。直到七十年代，关系数据库理论的提出，以表格形式组织数据，数据之间存在关联关系，具有了良好的结构化和规范化特性，成为主流数据库类型。

先来看一张数据库发展史图鉴：

随之高并发大数据时代的来临，数据库按照各种应用场景进行了更细粒度的拆分和演进，数据库细分领域的典型代表：

二 数据库名词概念

RDBS

1970 年的 6 月，IBM 公司的研究员埃德加・考特 （Edgar Frank Codd） 发表了那篇着名的《大型共享数据库数据的关系模型》（A Relational Model of Data for Large Shared Data Banks）的论文，拉开了关系型数据库（Relational DataBase Server）软件革命的序幕（之前是层次模型和网状模型数据库为主）。直到现在，关系型数据库在基础软件应用领域仍是最主要的数据存储方式之一。

关系型数据库创建在关系型数据模型的基础上，是借助于集合代数等数学概念和方法来处理数据的数据库。在关系型数据库中，实体以及实体间的联系均由单一的结构类型来表示，这种逻辑结构是一张二维表。关系型数据库以行和列的形式存储数据，这一系列的行和列被称为表，一组表组成了数据库。

NoSQL

NoSQL（Not Only SQL） 数据库也即非关系型数据库，它是在大数据的时代背景下产生的，它可以处理分布式、规模庞大、类型不确定、完整性没有保证的 “杂乱” 数据，这是传统的关系型数据库远远不能胜任的。NoSQL 数据库并没有一个统一的模型，是以牺牲事务机制和强一致性机制，来获取更好的分布式部署和横向扩展能力，使其在不同的应用场景下，对特定业务数据具有更强的处理性能。常用数据模型示例如下：

NewSQL

NewSQL 是一类新的关系型数据库， 是各种新的可扩展和高性能的数据库的简称。它不仅具有 NoSQL 数据库对海量数据的存储管理能力，同时还保留了传统数据库支持的 ACID 和 SQL 特性，典型代表有 TiDB 和 OceanBase。

OLTP

联机事务处理过程 (On-Line Transaction Processing)：也称为面向交易的处理过程，其基本特征是前台接收的用户数据可以立即传送到计算中心进行处理，并在很短的时间内给出处理结果，是对用户操作快速响应的方式之一。

OLAP

联机分析处理（On-Line Analytical Processing）是一种面向数据分析的处理过程，它使分析人员能够迅速、一致、交互地从各个方面观察信息，以达到深入理解数据的目的。它具有 FASMI (Fast Analysis of Shared Multidimensional Information)，即共享多维信息的快速分析的特征。

关于 OLTP 和 OLAP 的区别，借用一张表格对比如下：

HTAP (Hybrid Transactional/Analytical Processing) 混合型数据库基���新的计算存储框架，能够同时支撑 OLTP 和 OLAP 场景，避免传统架构中大量数据交互造成的资源浪费和冲突。

三 领域数据库

列式数据库

传统的以行形式保存的数据主要满足 OLTP 应用，列形式保存的数据主要满足以查询为主的 OLAP 应用。在列式数据库中，数据按列存储，而每个列中的数据类型相同。这种存储方式使列式数据库能够更高效地处理大量的数据，特别是需要进行大规模的数据分析和处理时（如金融、医疗、电信、能源、物流等行业）。

两种存储结构的区别如下图：

列式数据库的主要优点：

• 更高的压缩比率：由于每个列中的数据类型相同，列式数据库可以使用更高效的压缩算法来压缩数据（压缩比可达到 5～20 倍），从而减少存储空间的使用。

• 更快的查询速度：列式数据库可以只读取需要的列，而不需要读取整行数据，从而加快查询速度。

• 更好的扩展性：列式数据库可以更容易地进行水平扩展，即增加更多的节点和服务器来处理更大规模的数据。

• 更好的数据分析支持：由于列式数据库可以处理大规模的数据，它可以支持更复杂的数据分析和处理操作，例如数据挖掘、机器学习等。

列式数据库的主要缺点：

• 更慢的写入速度：由于数据是按列存储，每次写入都需要写入整个列，而不是单个行，因此写入速度可能较慢。

• 更复杂的数据模型：由于数据是按列存储，数据模型可能比行式数据库更复杂，需要更多的设计和开发工作。

列式数据库的应用场景：

• 金融：金融行业的交易数据和市场数据，例如股票价格、外汇汇率、利率等。列式数据库可以更快速地处理这些数据，并且支持更复杂的数据分析和处理操作，例如风险管理、投资分析等。

• 医疗：医疗行业的病历数据、医疗图像和实验数据等。列式数据库可以更高效地存储和处理这些数据，并且支持更复杂的医学研究��分析操作。

• 电信：电信行业的用户数据和通信数据，例如电话记录、短信记录、网络流量等。列式数据库可以更快速地处理这些数据，并且支持更复杂的用户行为分析和网络优化操作。

• 能源：能源行业的传感器数据、监测数据和生产数据等。列式数据库可以更高效地存储和处理这些数据，并且支持更复杂的能源管理和控制操作。

• 物流：物流行业的运输数据、库存数据和订单数据等。列式数据库可以更快速地处理这些数据，并且支持更复杂的物流管理和优化操作。

总之，列式数据库是一种高效处理大规模数据的数据库管理系统，但需要权衡写入速度、数据模型复杂度和成本等因素。 随着传统关系型数据库与新兴的分布式数据库不断的发展，列式存储与行式存储会不断融合，数据库系统呈现双模式数据存放方式。

时序数据库

时序数据库全称为时间串行数据库 (Time Series Database)，用于存储和管理时间串行数据的专业化数据库，是优化用于摄取、处理和存储时间戳数据的数据库。其跟常规的关系数据库 SQL 相比，最大的区别在于：时序数据库是以时间为索引的规律性时间间隔记录的数据库。

时序数据库在物联网和互联网应用进程监控（APM）等场景应用比较多，以监控数据采集来举例，如果数据监控数据采集时间间隔是 1s，那一个监控项每天会产生 86400 个数据点，若有 10000 个监控项，则一天就会产生 864000000 个数据点。在物联网场景下，这个数字会更大，整个数据的规模，是 TB 甚至是 PB 级的。

时序数据库发展史：

当下最常见的 Kubernetes 容器管理系统中，通常会搭配普罗米修斯（Prometheus）进行监控，Prometheus 就是一套开源的监控 & 报警 & 时间串行数据库的组合。

图数据库

图数据库（Graph Database）是基于图论实现的一种新型 NoSQL 数据库。它的数据存储结构和数据的查询方式都是以图论为基础的。图论中图的基本元素为节点和边，在图数据库中对应的就是节点和关系。

图数据库在反欺诈多维关联分析场景，社交网络图谱，企业关系图谱等场景中可以做一些非常复杂的关系查询。这是由于图数据结构表现的是实体联系本身，它表现了现实世界中事物联系的本质，它的联系在节点创建时就已经创建，所以在查询中能以快捷的路径返回关联数据，从而表现出非常高效的查询性能。

目前市面上较为流行的图数据库产品有以下几种：

与传统的关系数据库相比，图数据库具有以下优点：

1. 更快的查询速度：图数据库可以快速遍历图数据，找到节点之间的关联和路径，因此查询速度更快。

2. 更好的扩展性：图数据库可以轻松地扩展到大规模的数据集，因为它们可以分布式存储和处理数据。

3. 更好的数据可视化：图数据库可以将数据可视化为图形，使用户更容易理解和分析数据。

4. 更好的数据一致性：图数据库可以确保数据的一致性，因为它们可以在节点和边之间创建强制性的关系。

四 数据结构设计

前面简单介绍了数据库相关的基础知识，下面再介绍几种我们常见的数据结构设计相关的应用实践：拉链表，位运算和环形队列。

4.1 拉链表

拉链表是一种数据仓库中常用的数据模型，用于记录维度数据的变化历史。我们以一个人员变动的场景举例，假设有一个员工信息表，其中包含了员工的姓名、工号、职位、部门、入职时间等信息。如果需要记录员工的变动情况，就可以使用拉链表来实现。

首先，在员工信息表的基础上添加两个字段：生效时间和失效时间。当员工信息发生变动时，不再添加一条记录，而是修改原有记录的失效时间，同时添加一条新的记录。如下表所示：

这里的生效时间指的是该记录生效的时间，失效时间指的是该记录失效的时间。例如，张三最初是技术部经理，生效时间为入职时间，失效时间为 2012 年底，之后晋升为技术部总监，生效时间为 2013 年初，失效时间为 2015 年底，最后又晋升为技术部总经理，生效时间为 2016 年初，失效时间为 9999 年底。

通过这种方式，可以记录员工变动的历史信息，并能够方便地查询某个时间点的员工信息。例如，如果需要查询张三在 2014 年的职位和部门信息，只需查询生效时间小于 2014 年且失效时间大于 2014 年的记录即可。

拉链表通常包括以下几个字段：

1.主键：唯一标识每个记录的字段，通常是一个或多个列的组合。 2.生效时间：记录的生效时间，即该记录开始生效的时间。 3.失效时间：记录的失效时间，即该记录失效的时间。 4.版本号：记录的版本号，用于标识该记录的版本。 5.其他维度属性：记录的其他维度属性，如客户名、产品名、员工名等。

当一个记录的维度属性发生变化时，不再添加一条记录，而是修改原有记录的失效时间，同时添加一条新的记录。新记录的生效时间为变化的时间，失效时间为 9999 年底。这样就能够记录每个维度属性的历史变化信息，同时保证查询时能够正确获取某个时间点的维度属性信息。

拉链表与传统的流水表相比，它们的主要区别在于：

1. 数据结构不同：流水表是一张只有添加和更新操作的表，每次更新都会添加一条记录，记录中包含了所有的历史信息。而拉链表则是一张有添加、更新和删除操作的表，每个记录都有一个生效时间段和失效时间段，记录的历史信息通过时间段的变化来体现。

2. 查询方式不同：流水表的查询方式是基于时间点的查询，即查询某个时间点的记录信息。而拉链表的查询方式是基于时间段的查询，即查询某个时间段内的记录信息。

3. 存储空间不同：由于流水表需要记录所有历史信息，所以存储空间相对较大。而拉链表只记录生效时间段和失效时间段，所以存储空间相对较小。

4. 数据更新方式不同：流水表只有添加和更新操作，每次更新都会添加一条记录，不会对原有记录进行修改。而拉链表有添加、更新和删除操作，每次更新会修改原有记录的失效时间，同时添加一条新的记录。

4.2 巧用位运算

借助于计算机位运算的特性，可以巧妙的解决某些特定问题，使实现更加优雅，节省存储空间的同时，也可以提高运行效率，典型应用场景：压缩存储、位图索引、数据加密、图形处理和状态判断等，下面介绍几个典型案例。

4.2.1 位运算

• 使用位运算实现开关和多选项叠加（资源权限）等应用场景。一个 int 类型有 32 个位，理论上可以表示 32 个开关状态或业务选项；以用户每个月的签到场景举例：用一个 int 字段来表示用户一个月的签到情况，0 表示未签到，1 表示签到。想知道某一天是否签到，则只需要判断对应的比特位上是否为 1。计算一个月累计签到了多少次，只需要统计有多少个比特位为 1 就可以了。这种设计巧妙的数据存储结构在后面的位图（BitMap）中，还会进行更为详细的介绍。

• 使用位运算实现业务优先级计算：

• 其它使用位运算的典型场景：HashMap 中的队列长度的设计和线程池 ThreadPoolExcutor 中使用 AtomicInteger 字段 ctl，存储当前线程池状态和线程数量（高 3 位表示当前线程的状态，低 29 位表示线程的数量）。

4.2.2 BitMap

位图（BitMap）是一种常用的数据结构，在索引，数据压缩等方面有广泛应用。基本思想就是用一个 bit 位来标记某个元素对应的 Value，而 Key 即是该元素。由于采用了 Bit 为单位来存储数据，因此可以大大节省存储空间，是少有的既能保证存储空间又能保证查找速度的数据结构（而不必空间换时间）。

举个例子，假设有这样一个需求：在 20 亿个随机整数中找出某个数 m 是否存在其中，并假设 32 位操作系统，4G 内存，在 Java 中，int 占 4 字节，1 字节 = 8 位（1 byte = 8 bit）。

• 如果每个数字用 int 存储，那就是 20 亿个 int，因而占用的空间约为 (2000000000*4/1024/1024/1024)≈7.45G

• 如果按位存储就不一样了，20 亿个数就是 20 亿位，占用空间约为 (2000000000/8/1024/1024/1024)≈0.233G

存储空间可以压缩节省 31 倍！那么它是如何通过二进制位实现数字标记的呢？ 其原理是用每个二进制位（下标）表示一个真实数字，0 表示不存在，1 表示存在，这样我们可以很容易表示 {1,2,4,6} 这几个数：

计算机内存分配的最小单位是字节，也就是 8 位，那如果要表示 {12,13,15} 怎么办呢？可以另申请一个字节 b [1]：

通过一个二维数组来实现位数叠加，1 个 int 占 32 位，那么我们只需要申请一个 int 数组长度为 int index [1+N/32] 即可存储，其中 N 表示要存储的这些数中的最大值：

index [0]：可以表示 0\~31

index [1]：可以表示 32\~63

index [2]：可以表示 64\~95

以此类推 ... 如此一来，给定任意整数 M，那么 M/32 就得到下标，M%32 就知道它在此下标的哪个位置。

BitMap 数据结构通常用于以下场景：

1. 压缩存储大量布尔值：BitMap 可以有效地压缩大量的布尔值，从而减少内存的使用；

2. 快速判断一个元素是否存在：BitMap 可以快速地判断一个元素是否存在，只需要查找对应的位即可；

3. 去重：BitMap 可以用于去重操作，将元素作为索引，将对应的位设置为 1，重复元素只会对应同一个位，从而实现去重；

4. 排序：BitMap 可以用于排序，将元素作为索引，将对应的位设置为 1，然后按照索引顺序遍历位数组，即可得到有序的元素串行；

5. ElasticSearch 和 Solr 等搜索引擎中，在设计搜索剪枝时，需要保存已经搜索过的历史信息，可以使用位图减小历史信息数据所占空间；

4.2.3 布隆过滤器

位图（Bitmap）这种数据存储结构，如果数据量大到一定程度，比如 64bit 类型的数据，简单算一下存储空间就知道，海量硬件资源要求，已经不太现实了：

所以另一个着名的工业实现 - 布隆过滤器（Bloom Filter）出现了。如果说 BitMap 对于每一个可能的整型值，通过直接寻址的方式进行映射，相当于使用了一个哈希函数，那布隆过滤器就是引入了 k (k> 1 ) 个相互独立的哈希函数，保证在给定的空间和误判率情况下，完成元素判重的过程。下图中是 k = 3 时的布隆过滤器：

布隆过滤器的内部依赖于哈希算法，当检测某一条数据是否见过时，有一定概率出现假阳性（False Positive），但一定不会出现假阴性（False Negative）。也就是说，当 布隆过滤器认为一条数据出现过，那么该条数据很可能出现过；但如果布隆过滤器认为一条数据没出现过，那么该条数据一定没出现过。布隆过滤器通过引入一定错误率，使得海量数据判重在可以接受的内存代价中得以实现。

上图中，x,y,z 经由哈希函数映射将各自在 Bitmap 中的 3 个位置置为 1，当 w 出现时，仅当 3 个标志位都为 1 时，才表示 w 在集合中。图中所示的情况，布隆过滤器将判定 w 不在集合中。

常见实现

• Java 中 Guava 工具包中实现；

• Redis 4.0 开始以插件形式提供布隆过滤器功能；

适用场景

• 网页爬虫对 URL 的去重，避免爬去相同的 URL 地址，比如 Chrome 浏览器就是使用了一个布隆过滤器识别恶意链接；

• 垃圾邮件过滤，从数十亿个垃圾邮件列表中判断某邮箱是否是杀垃圾邮箱；

• 解决数据库缓存击穿，黑客攻击服务器时，会构建大量不存在于缓存中的 key 向服务器发起请求，在数据量足够大的时候，频繁的数据库查询会导致挂机；

• 谷歌 Bigtable、Apache HBase、Apache Cassandra 和 PostgreSQL 使用布隆过滤器来减少对不存在的行或列的磁盘查找；

• 秒杀系统，查看用户是否重复购买；

4.2.4 小结

• 位运算有着执行速度快，占用空间小，代码实现简洁等优点，往往能起到四两拨千斤的效果。同样也有着代码可读性差，使用范围和可维护性受限等不足；

• 在 BitMap 中，占用空间大小还与实际应用场景有关，这种结构无法容忍误判，只能判断一个元素是否存在，如果数据离散度过高，空间利用率反而更低；

• 布隆过滤器则有着空间利用率高，可以容忍一定的误判率的优点。与 BitMap 相比，也存在着无法删除元素，误判率无法达到 0 等不足；

4.3 环形队列

环形队列是一种用于表示一个固定尺寸、头尾相连的数据结构，很适合缓存数据流。在通信开发（Socket，TCP/IP，RPC 开发），在内核的进程间通信（IPC），视频音频播放等各种场景中，都有其身影。日常开发过程中使用的 Dubbo、Netty、Akka、Quartz、ZooKeeper 、Kafka 等各种中间件，也都有环形队列的思想。下面介绍两种常用的环形数据结构：Hash 环和时间轮。

4.3.1 一致性 Hash 环

先来看一下，典型 Hash 算法结构如下：

以上图 Hash 策略为例，当节点数 N 发生变化的时候 之前所有的 hash 映射几乎全部失效，如果集群是无状态的服务，倒是没什么事情，但是如果是分布式缓存这种场景，就会导致比较严重的问题。比如 Key1 原本是路由到 Node1 上，命中缓存的 Value1 数据。但是当 N 节点变化后，Key1 可能就路由到了 Node2 节点，这就产生了缓存数据无法命中的问题。而无论是机器故障还是缓存扩容，都会导致节点数的变化。

如何解决上面场景的问题呢？就是接下来介绍的一致性 Hash 算法。

一致性哈希将整个哈希值空间组织成一个虚拟的圆环，假设某哈希函数 H 的值空间为 0-2^32-1（即哈希值是一个 32 位无符号整型），所有的输入值都被映射到 0-2^32-1 之间，组成一个圆环。整个哈希空间环如下：

路由数据的过程如下：将数据 key 使用相同的函数 Hash 计算出哈希值，并确定此数据在环上的位置，从此位置沿环顺时针 “行走”，遇到的第一个节点就是其应该定位到的服务器。如果某个节点的服务器故障，其影响范围也不再是所有集群，而是限定在故障节点与其上游节点的部分区域。

当某个节点宕机后，原本属于它的请求都会被重新 hash 映射到下游节点，会突然造成下游节点压力过大有可能也会造成下游节点宕机，从而容易形成雪崩，为此引入了虚拟节点来解决这个问题。

根据 Node 节点生成很多的虚拟节点分布在圆环上，，一个真实节点映射对应多个虚拟节点。这样当某个节点挂了后原本属于它的请求，会被均衡的分布到其他节点上降低了产生雪崩的情况，也解决了物理节点数少，导致请求分布不均的问题。

带有虚拟节点的 Hash 环：

一致性 Hash 算法由于均衡性，持久性的映射特点被广泛应用于负载均衡领域，比如 nginx、dubbo 等内部都有一致性 hash 的实现。

4.3.2 时间轮分片

时间轮（TimeWheel）是一种实现延迟功能（定时器）的精妙的算法，可以实现高效的延时队列。以 Kafka 中的时间轮实现方案为例，它是一个存储定时任务的环形队列，底层采用数组实现，数组中的每个元素可以存放一个定时任务列表（TimerTaskList）。TimerTaskList 是一个环形的双向链表，链表中的每一项表示的都是定时任务项（TimerTaskEntry），其中封装了真正的定时任务 TimerTask。

通过上图可以发现，时间轮算法不再任务队列作为数据结构，轮询线程不再负责遍历所有任务，而是仅仅遍历时间刻度。时间轮算法好比指针不断在时钟上旋转、遍历，如果一个发现某一时刻上有任务（任务队列），那么就会将任务队列上的所有任务都执行一遍。

假设相邻 bucket 到期时间的间隔为 bucket=1s，从 0s 开始计时，1s 后到期的定时任务挂在 bucket=1 下，2s 后到期的定时任务挂在 bucket=2 下，当检查到时间过去了 1s 时，bucket=1 下所有节点执行超时动作，当时间到了 2s 时，bucket=2 下所有节点执行超时动作。时间轮使用一个表盘指针（pointer），用来表示时间轮当前指针跳动的次数，可以用 tickDuration * (pointer + 1) 来表示下一次到期的任务，需要处理此 bucket 所对应的 TimeWheel 中的所有任务。

时间轮的优点

1. 任务的添加与移除，都是 O (1) 级的复杂度；

2. 只需要有一个线程去推进时间轮，不会占用大量的资源；

3. 与其他任务调度模式相比，CPU 的负载和资源浪费减少；

适用场景

时间轮是为解决高效调度任务而产生的调度模型。在周期性定时任务，延时任务，通知任务等场景都可以发��效用。

五 总结

本文针对数据存储相关名词概念进行了���释，重点介绍了数据库技术的发展史。为了丰富文章的可读性以及实用性，又从数据结构设计层面进行了部分技术实战能力的外延扩展，阐述了拉链表，位运算，环形队列等相关数据结构在软件开发领域的应用，希望本文给你带来收获。

[financialinsightdailycom]

What is Chainbase: Bridging Security and Cross-Chain Capabilities in Blockchain

Curious about what is Chainbase and why it's gaining popularity? This article will explore how Chainbase enhances blockchain applications by offering top-notch security and cross-chain functionality. Read on to discover more at Financial Insight Daily.

Chainbase’s Dual-Chain Security Architecture

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Real-Time Data Management via APIs

Another key feature of Chainbase is its API ecosystem, which provides real-time access to blockchain data, including tokens, NFTs, and transaction records. These APIs streamline the process of building decentralized applications by offering ready access to crucial blockchain data without requiring developers to build data pipelines from scratch.

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Cross-Chain Functionality for Seamless User Experience

Chainbase’s cross-chain functionality is particularly valuable for developers working in wallet management and DeFi (Decentralized Finance). The platform makes it possible for users to manage assets across multiple blockchains within a single interface, reducing the complexity and fragmentation often associated with multi-chain asset management.

In DeFi, cross-chain functionality is crucial for enabling lending, borrowing, and moving assets across various networks, which increases liquidity and flexibility for users. For developers, this opens the door to creating more dynamic and interoperable decentralized applications that can span multiple blockchain ecosystems.

Developer Tools to Support Blockchain Innovation

Chainbase goes beyond security and cross-chain functionality by offering a suite of developer tools designed to simplify the process of creating decentralized applications. These tools include debugging systems, log tracking, and monitoring dashboards, all of which help developers identify and resolve issues quickly.

The platform's no-configuration setup ensures that developers, whether working in teams or individually, can focus on innovating rather than spending time on complex infrastructure management. Chainbase’s streamlined environment allows developers to bring their ideas to life faster and more efficiently, making it a top choice for blockchain innovators.

🚀☄️💥The story continues on our website. Click here to read the full article: https://financialinsightdaily.com/what-is-chainbase/

[cromacampuseducational]

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