Imply’s Post

Check out how to use Upserts in Imply Polaris Upserts are particularly useful for two key reasons: 1. Data Consistency. Upserts ensure that data remains consistent by either updating existing records or adding new ones. 2. Efficiency. Upserts combine the insert and update operations into a single action, reducing the number of database queries and improving performance. Learn more here: https://lnkd.in/gFsAce8D #Imply #StreamingData #DatabaseUpserts #DataConsistency #Kafka #MSK #RealTimeAnalytics #DataAnalytics #RealTimeData #ApacheDruid

We’ve built the LiveSync database connector to make it easy to work with database generated events, and solve the things that make it hard to work with the more traditional approach of using Change Data Capture (CDC). In our latest blog post, Ably Engineer Zak Knill explains how it works and why we made some intentional choices about why it should be different to CDC. 🌟 Simplified joins & encapsulation: Easily manage complex data models without the headache of gluing events back together. 🌟 Enhanced access control: Control exactly which data gets published and who sees it through Ably's powerful capabilities. 🌟 Optimized data delivery: Publish only the data your frontend needs, reducing oversharing and improving performance. Read it here 👇

Ensuring top-notch database performance while aiming to cut down costs? 🚀 Look no further! With Enteros' UpBeat you can revolutionize your database management using automated database performance diagnostics and gain actionable insights for database performance. 📊⚙️💼 Read more about it here: https://lnkd.in/g-kJ-MGY #EnterosUpBeat #DatabasePerformance #CostReduction #AutomatedDiagnostics #ActionableInsights #AdvancedAlgorithms #DatabaseOptimization #EfficiencyEnhancement #DataDrivenSolutions #DigitalRevolution

Ensuring top-notch database performance while aiming to cut down costs? 🚀 Look no further! With Enteros' UpBeat you can revolutionize your database management using automated database performance diagnostics and gain actionable insights for database performance. 📊⚙️💼 Read more about it here: https://lnkd.in/g-kJ-MGY #EnterosUpBeat #DatabasePerformance #CostReduction #AutomatedDiagnostics #ActionableInsights #AdvancedAlgorithms #DatabaseOptimization #EfficiencyEnhancement #DataDrivenSolutions #DigitalRevolution

TiDB 8.1 is out, some notable highlights (my personal favs). - DXF enabled by default (eg., 40TB table CREATE INDEX or IMPORT uses the Distributed eXecution Framework) - TiCDC now supports Debezium - Seamless data migration from MySQL 8.x - Global sort - complements DXF - Bulk creat tables 10x faster (this is quite challenging I found in a distributed database)

The "better" approach between read-optimized and write-optimized databases strongly depends on the specific use case. Traditional relational databases excel in scenarios where data consistency and integrity are paramount. They typically provide strong ACID guarantees, making them ideal for financial transactions, inventory management, and other applications where read and write operations are required to be accurate and consistent. On the other hand, LSM tree-based databases, like the one discussed in the document, are typically write-optimized. They excel in high-volume, write-intensive workloads as they utilize a log-structured approach that minimizes write amplification and optimizes for write speeds. These databases are often used in logging, event data management, and other scenarios where data is written more frequently than it is read or updated. However, this doesn't mean LSM tree-based databases are not capable of efficient reads. By sorting data on key before timestamp and using background compaction to limit read amplification, LSM trees can also provide efficient read operations. But they might not provide the same level of consistency guarantees as traditional databases. We used LSMT for our home grown distributed KV system Juno at PayPal that served 5M operations/sec at peak back then. Typical Issue is not read vs write but the concurrency management and if there are hot keys with multiple updates….

𝗨𝗯𝗲𝗿 𝗰𝗿𝗲𝗮𝘁𝗲𝗱 𝘁𝗵𝗲𝗶𝗿 𝗼𝘄𝗻 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗳𝗿𝗼𝗺 𝘀𝗰𝗿𝗮𝘁𝗰𝗵. It was called 𝗦𝗰𝗵𝗲𝗺𝗮𝗹𝗲𝘀𝘀 𝗗𝗕. As part of it, they wanted highly-available write operations. This was difficult to achieve…. …because all writes go to the Leader Node. And you can’t ensure high availability if the Leader Node goes down. Uber made it possible by using a smart but simple technique called 𝗕𝘂𝗳𝗳𝗲𝗿𝗲𝗱 𝗪𝗿𝗶𝘁𝗲𝘀. In a nutshell, Buffered Writes means that every write request is stored on at least two nodes - 𝗣𝗿𝗶𝗺𝗮𝗿𝘆 𝗟𝗲𝗮𝗱𝗲𝗿 and 𝗦𝗲𝗰𝗼𝗻𝗱𝗮𝗿𝘆 𝗟𝗲𝗮𝗱𝗲𝗿. Here’s how it works: ✅ The client makes a request to the request handler ✅ The request handler sends the write requests to the Secondary Leader. The data is stored in a special Buffer Table on the Secondary Leader. ✅ Then, it also sends the write request to the Primary Leader. Only if both writes are successful, the client receives a successful write confirmation. ✅ The Primary Leader’s job is to replicate the data. ✅ But if the leader goes down before the async replication is successful, the Secondary Leader serves as a temporary backup of data ✅ A Background Worker monitors the Primary Follower for when the record appears after replication ✅ Once the record appears on the Primary Follower, the Background Worker goes ahead and deletes the record from the Buffer Table A few important points to note over here: - The number of secondary leaders is configurable - The secondary leader is chosen at random - Buffered writes use idempotency. If there are multiple writes with the same identifying fields, it doesn’t matter how many times the request is made. 𝗦𝗼 - 𝘄𝗵𝗮𝘁 𝗱𝗼 𝘆𝗼𝘂 𝘁𝗵𝗶𝗻𝗸 𝗮𝗯𝗼𝘂𝘁 𝗯𝘂𝗳𝗳𝗲𝗿𝗲𝗱 𝘄𝗿𝗶𝘁𝗲𝘀? 𝗛𝗼𝘄 𝘄𝗼𝘂𝗹𝗱 𝗵𝗮𝘃𝗲 𝘆𝗼𝘂 𝗵𝗮𝗻𝗱𝗹𝗲𝗱 𝗮 𝘀𝗶𝗺𝗶𝗹𝗮𝗿 𝘀𝗶𝘁𝘂𝗮𝘁𝗶𝗼𝗻? And if you are interested in System Design case studies like this, consider subscribing to my free weekly newsletter. It's much more detailed and organized. Here's the link: https://lnkd.in/g-Y6Evsp

Streamlining database conversions with small parameter LLMs requires a strategic approach. Discover the best practices for data preprocessing, model training, and collaborative optimization in our latest blog post. https://lnkd.in/gbKqtw-m #newtglobal #DMAP #database #llm #optimization

TimescaleDB 2.14.0 is a game-changer for all our users, making data compression simpler, more flexible, and incredibly efficient. Gone are the days when adjusting your database's compression settings was a time-consuming and impractical process. 🔄 With this new launch, modifying your compression settings on the fly has never been easier. Our latest version offers significant improvements, enabling new chunks (data partitions or smaller tables) to immediately adopt updated settings without the hassle of decompressing and re-compressing your data. This means your application can evolve seamlessly alongside your data requirements, granting you unprecedented flexibility. 🌟 Experience how our innovative per-chunk compression can revolutionize your data efficiency, saving on costs while managing your data more effectively. Compression has always been one of Timescale’s star features, and with this release, it shines even brighter. ✨ Read More 🔥 https://lnkd.in/gurNNvR6 #DataCompression #PostgreSQL #Data

Did you have difficulty selecting the right database? I have consolidated points, which helps in making the right decision. I'm sharing this with you all so that this critical decision can be taken quickly. Here's an expanded guide with additional considerations: 1. Data Integrity: Prioritize databases that enforce data integrity constraints, ensuring accuracy and reliability in your data. 2. Scalability:  Assess the database's ability to scale horizontally or vertically based on the growth of your application. 3. Distributed Network:  Consider databases that excel in distributed environments, providing seamless operation across multiple nodes. 4. Support:  Evaluate the level of support available, including documentation, community forums, and professional support services. 5. Budget: Align your database choice with budget constraints, considering licensing costs, maintenance, and hardware expenses. 6. Language Compatibility: Ensure compatibility with programming languages used in your stack for seamless integration. 7. Transactional Support:  Choose databases that offer substantial ACID compliance for transactional consistency. 8. CAP Theorem Consideration:  Balance between Consistency, Availability, and Partition Tolerance based on your application's requirements. 9. Simplicity:  Opt for databases that align with your team's expertise and are easy to manage. 10. OLAP/OLTP Compatibility:  Distinguish between databases optimized for Online Analytical Processing (OLAP) and Online Transactional Processing (OLTP). 11. Query Complexity: Consider the complexity of your queries and choose databases that can handle them efficiently. 12. Data Size: Evaluate the anticipated data size to ensure the database can handle the volume without compromising performance. 13. Data Structure Variety: Depending on your use case, choose a database that supports specific data structures like time series or graphs. In the ever-evolving landscape of technology and new databases released every week, making an informed decision is key. Share your thoughts or add more factors to consider! Let's build smarter together.  #databases #softwaredevelopment #databaseoptimization #databasemanagement