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Photonics Speeds Up Data Center AI

Photonics plays a vital role in accelerating AI in data centers, with the optical components market reaching $17 billion in revenue last year.
Datacom sector, particularly AI-driven data centers, now contributes over 60% to the optical components market, fueling the growth of optical technologies.
The demand for high-bandwidth, low-latency networking solutions in AI data centers driven by the exponential growth of Large Language Models (LLMs).
Transition from pluggable optics to Co-Packaged Optics (CPO) in scale-out network can significantly reduce power consumption and increase GPU density.
Reliability is crucial when moving from copper to optics to CPO, especially in AI data centers where large volumes and high yields are imperative.
Scale-up interconnects in AI data centers currently rely on copper-based solutions, but the shift towards CPO is imminent for future optical requirements.
TSMC is deeply engaged in the development of AI chips and is preparing for co-packaged optics in their technology roadmap.
The optical industry foresees widespread adoption of Co-Packaged Optics (CPO) by the 2030s, with the market projected to reach $5 billion by 2030.
Early entrants like Broadcom, Marvell, Ayar Labs, Celestial AI, and Lightmatter stand to benefit from the growing CPO market alongside laser suppliers like Coherent.
By the mid-2030s, it is predicted that all interconnects in data centers will be optical and Co-Packaged Optics will be integral to the infrastructure.

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Dev

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Cracking MySQL Performance: What is Indexing and Why It Matters

Indexes in MySQL act as a table of contents for the database, allowing for quick searching and retrieval of data.
An experiment on 80,000+ records revealed that adding an index on a column like city in MySQL resulted in a significant performance improvement.
Without indexing, a query took an average of ~420ms with a full table scan, while with indexing, the average time reduced to ~8ms with an index lookup.
Key takeaways: Indexes reduce query execution time, EXPLAIN is vital for SQL performance debugging, but indexes can slow down write operations slightly.

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Amazon

273

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Data masking and performance improvements in AWS DMS 3.5.4

AWS Database Migration Service (AWS DMS) version 3.5.4 introduces data masking for enhanced security and improved data validation performance features.
Data masking allows for secure migration by transforming sensitive data at the column level, helping comply with regulations like GDPR.
Three flexible data transformation rules – Digits Mask, Digits Randomize, and Hashing Mask – are implemented in AWS DMS 3.5.4.
Enhanced data validation feature in AWS DMS 3.5.4 uses techniques like fast partition validation to ensure data integrity during migrations.
The performance improvements in data validation lead to resource redistribution, decreased network usage, and efficiency for wide tables without LOB data types.
Enhanced data validation feature is available for specific migration paths like Oracle to PostgreSQL, SQL Server to PostgreSQL, etc.
Benchmarking tests showed significant reductions in TaskMemoryUsage and CPU utilization with AWS DMS 3.5.4 compared to 3.5.3.
Authors include specialists from Amazon Web Services who provide guidance and technical assistance on data migration and replication services.
Data masking and enhanced data validation features in AWS DMS 3.5.4 offer increased security and performance benefits for database migrations.
Customers are encouraged to test these features and provide feedback on their effectiveness for specific use cases.

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Medium

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Control Containers Like Rows

A developer created Jagura as a proof-of-concept to manage containers using SQL commands instead of traditional methods like shell scripts or dashboards.
Jagura allows Docker container management through a custom execution engine that interprets SQL queries into Docker-level operations.
While Jagura doesn't aim to replace tools like Docker Compose or Kubernetes, it provides a unique and fun approach to container orchestration.
The project reflects the developer's desire to merge the declarative elegance of SQL with the operational aspects of container orchestration, offering a fresh perspective on managing containers.

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Dev

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I built the Cursor of SQL databases ⚡️

TurboSQL is a fast, AI-powered SQL editor designed for developers.
It offers lightning-fast performance and a keyboard-first design.
Smart organization allows queries to be neatly organized into folders and files.
TurboSQL includes an AI assistant that generates queries, explains SQL statements, and autocompletes queries based on the database schema.

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Dev

175

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Connect Any Postgres to Real-Time Analytics

Timescale built livesync for Postgres to stream data into Timescale Cloud with zero downtime and no application changes, solving the analytics-vs-stability dilemma.
Livesync performs historical backfills at 150GB/hour, captures live changes through CDC, and converts tables to hypertables for accelerated queries.
It allows for real-time analytics without complex ETL pipelines or risky migrations, offering columnar storage, compression, and time-partitioning capabilities.
Livesync enables streaming data from any Postgres instance into Timescale Cloud without impacting the production database.
It eliminates the need for downtime, risk, or performance hits to production, providing fast real-time analytics.
Livesync uses Postgres’ logical replication protocol with high-throughput ingestion and prepares data for analytics at scale.
Designed to be non-invasive, Livesync maintains your production environment untouched while streaming data for analytics.
It is scalable, engineered for high throughput, and seamlessly integrates into existing architectures without downtime.
Livesync is suitable for scenarios where zero downtime is crucial, or when real-time insights are needed without complex migrations.
Industries like financial services, IoT, and AWS-native architectures can benefit from Livesync for faster analytics without disruptions.

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Dbi-Services

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Set SQL Server Trace Flags Automatically and Smoothly

Setting SQL Server trace flags is crucial for performance tuning and special use cases, but persistence across restarts is often a challenge.
Enabling trace flags programmatically using DBCC TRACEON is session-based or until instance restart.
For persistence, trace flags need to be set as startup parameters.
Manually setting trace flags as startup parameters through SQL Server Configuration Manager or Registry editing is not ideal for automation.
A PowerShell function is developed to programmatically set SQL Server trace flags as startup parameters.
This function streamlines registry configurations for each SQL Server instance and can restart the SQL Server service for immediate effect.
The PowerShell function automates trace flag setup, avoiding redundant updates and human errors.
After execution, the trace flags are visible in the registry and SQL Server Configuration Manager.
The function intelligently detects existing trace flags and only sets missing ones.
By triggering a service restart, the trace flags become active immediately.

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Dbi-Services

Oracle: A possible method to address a slow query if there is time pressure

In time-sensitive situations where addressing a slow query quickly is necessary, it can be helpful to test if the query ran faster with a previous OPTIMIZER_FEATURES_ENABLE-setting to identify the root cause of the performance issue.
To find the optimizer bug fix causing a suboptimal plan, start by checking different OPTIMIZER_FEATURES_ENABLE settings to see when the query started running slow.
ASH data can provide insights into how long the query has been running slowly and guide the investigation.
By testing various OPTIMIZER_FEATURES_ENABLE settings, such as '11.2.0.1' to '11.2.0.2', you can pinpoint the version change that impacted query performance.
Identifying the specific bug fix that caused the suboptimal plan involves testing _fix_control settings introduced with the affected OPTIMIZER_FEATURES_ENABLE version.
Creating a script to test each _fix_control setting helps determine the exact bug fix responsible for the query slowdown.
Once the problematic bug fix is identified, such as bugno 9195582, implementing a workaround like disabling the fix through a hint can improve query performance.
Verifying the effectiveness of the workaround and creating a SQL patch for the query can help ensure that the performance issue is addressed and documented for future reference.
When applying workarounds, it is essential to make minimal changes and document them properly, aiming to resolve the root cause in the long term.
Tools like Oracle's SQLTXPLAIN can provide detailed analysis of plan changes due to optimizer settings and underscore parameters for in-depth query optimization.

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Dev

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#34 50 Advanced SQL Queries Every Developer Should Know

This article explores 50 advanced SQL queries covering window functions, recursive CTEs, pivoting, performance optimization, and more.
Window functions like ROW_NUMBER(), RANK(), DENSE_RANK(), and NTILE() help in performing computations across rows.
Functions like LEAD(), LAG(), FIRST_VALUE(), and LAST_VALUE() provide access to next/previous row values and first/last values in a window.
Advanced SQL techniques include running totals, moving averages, common table expressions (CTEs), and recursive queries.
Pivoting and unpivoting data is achieved through techniques like CASE, PIVOT, and UNPIVOT.
The article delves into advanced aggregates, JSON/XML handling, dynamic SQL, advanced joins, and set operations.
It covers topics like performance optimization, database administration, date & time operations, string manipulation, security permissions, and more.
Queries like UPSERT, MERGE, DELETE with JOIN, UPDATE from another table, and handling long-running queries are also discussed.
The article concludes with 20 additional advanced SQL queries, providing an extensive coverage of essential SQL techniques for developers.

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Dbi-Services

404

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YaK Core – The Holy Grail for Deploying Ansible Code Everywhere

YaK Core is a Multi-Platform open source Automation Tool that simplifies the deployment of Ansible playbooks through a clean UI and API.
It offers an intuitive interface where users can upload playbooks, configure parameters, and deploy them seamlessly across various platforms.
YaK Core consists of two parts: YaK Core, which is open source, and YaK Components, which can be installed on top.
YaK Core allows users to create their own YaK components and deploy them across all supported platforms.

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Hackernoon

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Fixing Garbled Text When Syncing Oracle to Doris with SeaTunnel 2.3.9

When using SeaTunnel 2.3.9 to sync data from Oracle to Doris, you may encounter garbled characters—especially if the Oracle database uses the ASCII character set.
The issue stems from how SeaTunnel reads data from Oracle. If Oracle is using a character set like ASCII, and you're syncing to Doris (which expects proper UTF-8 or other compatible encodings), Chinese characters can become unreadable.
The key is to intercept and re-encode the data when it is read from the Oracle ResultSet.
By adding a simple charset-switching mechanism and tweaking the JDBC source implementation, you can eliminate garbled characters when syncing Oracle data to Doris using SeaTunnel.

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Dev

161

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23ai - DBMS_SEARCH - pure SQL Sorcery | A promising alternative to global indexes.

23ai DBMS_SEARCH is a cool Oracle package that helps search across the entire database schema without the need for multiple indexes.
Instead of juggling 10 indexes and writing custom queries, DBMS_SEARCH allows the creation of one mega-index for effective searching.
The test case from Oracle Sample Schemas demonstrates how DBMS_SEARCH can be used in a partitioned table scenario.
Comparing the results and SQL plans with traditional queries, the DBMS_SEARCH indexed queries show improved performance.

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Dev

310

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Fetch Values from Previous Non-Null Value Rows — From SQL to SPL #24

The task is to create a list of account statuses for each day within a specific period based on a given query date
The SQL code uses recursive subqueries, window functions, and join statements to create the desired result
The SPL code avoids aggregating grouped accounts and focuses on calculating subsets of the groups
esProc SPL offers a free download for users to try

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Amazon

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Zupee implements Amazon Neptune to detect Wallet transaction anomalies in real time

Zupee, a skill-based gaming platform in India, integrated Amazon Neptune Database to detect anomalies in real-time for wallet transactions.
Their initial solution using a relational database showed limitations when processing millions of transactions, leading them to adopt a graph-based approach with Amazon Neptune.
Graph databases excel in managing interconnected data efficiently, allowing for complex relationship analysis without predefined joins.
Zupee leveraged Neptune's capabilities to process over 1 million wallet transactions daily, identifying suspicious patterns and flagging anomalies.
The graph data model in Neptune helps Zupee monitor wallet transactions by creating clusters of users and detecting unusual transaction behaviors.
Using Union Find algorithm, Zupee efficiently grouped associations within the platform, uncovering complex relationships.
Neptune's architecture enabled Zupee to detect duplicate accounts, shared payment instruments, and calculate appropriate incentives based on user authenticity.
Zupee optimized costs by rewarding genuine users and adjusting incentives for accounts with anomalies, ensuring fair distribution.
With Neptune, Zupee achieved less than a 50-millisecond response time for anomaly detection and now manages a vast network of over 5 million interconnected nodes and edges.
Authors Aman Kumar Bahl, Apoorv Mathur from Zupee, and Ajeet Dubey from AWS played key roles in leading data engineering, architecture design, and cloud-focused solutions for this integration.

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Amazon

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How Habby enhanced resiliency and system robustness using Valkey GLIDE and Amazon ElastiCache

Habby, a game studio, faced challenges with connection stability in their chat functionality using Amazon ElastiCache for Redis OSS publish/subscribe (Pub/Sub) during infrastructure changes.
To address this, they adopted Valkey GLIDE, a client library for Amazon ElastiCache, which improved system reliability, handling 500,000 concurrent players and 100,000 queries per second during failover testing.
Habby's messaging system architecture includes WebSocket servers, IM services, REST API servers, and Amazon ElastiCache cluster for message delivery and interaction management.
The system uses Valkey GLIDE for client communication with Amazon ElastiCache, improving message delivery through unicast, broadcast, and multicast distribution types.
Valkey GLIDE provides features like robust failover system, direct primary node subscription, customizable retry configuration, and independent subscription client for reliability and scalability.
The article details Player class implementation, connection management, message sending and receiving, with emphasis on system structuring and configuration.
Valkey GLIDE migration resulted in efficient failover handling, supporting 500,000 concurrent players and 100,000 QPS load with 500 node capacity and sharded Pub/Sub architecture of Amazon ElastiCache.
The implementation completed in two weeks, enhancing system reliability and performance, laying a foundation for scalability and high performance for Habby's message delivery system.
The authors include Shuxiang Zhao, Haoyang Yu from Habby, Lili Ma, Xin Zhang, and Siva Karuturi from AWS, specializing in game backend, software development, database solutions, and solutions architecture.
Overall, the Valkey GLIDE and Amazon ElastiCache integration significantly improved system reliability and performance for Habby's game studio, offering a fault-tolerant architecture for message delivery.

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