Tips for Database Performance Optimization

There is a common misconception that the performance of a heavy query in databases with hundreds of terabytes of data can be improved by adding more CPU and RAM. This is true until the data, which is accessed by the query, fits the OS page cache (the size of this cache is proportional to the available RAM), and the same (or similar) queries are executed repeatedly, so they could read the data from the OS page cache instead of reading it from persistent storage. If the query needs to read hundreds of terabytes of data, then it cannot fit RAM on typical hosts. This means that the performance of such queries is limited by the disk read speed in this case, and it cannot be improved by adding more RAM and CPU. Which techniques do exist for speeding up heavy queries, which need to read a lot of data? 1. Compression. It is better to spend additional CPU time on decompression of the compressed data stored on disk instead of waiting for much longer until the uncompressed data is read from disk. For example, typical compression ratio for real production logs is 10x-50x. This allows speeding up heavy queries by 10x-50x compared to the case when the data is stored on disk in uncompressed form. 2. Physically grouping and sorting similar rows close to each other, and compress blocks of such rows. This increases the compression ratio compared to the case when rows are stored and compressed without additional grouping and sorting. 3. Physically storing per-column data in distinct locations (files). This is known as column-oriented storage. Then the query needs to read the data only for the referred columns, while skipping the data for the rest of the columns. 4. Using time-based partitioning, bloom filters, min-max indexes and coarse-grained indexes for skipping reading data blocks, which do not have rows needed for the query. These techniques allow increasing heavy query performance by 1000x and more on systems where the bottleneck is disk read IO bandwidth. All these techniques are automatically used by VictoriaLogs for increasing performance of heavy queries over hundreds of terabytes of logs.

If you’re clustering or partitioning your data on timestamp-based keys—especially in systems like BigQuery or Snowflake, etc. this diagram should look familiar 👇 Hotspots in partitioned databases are one of those things you don’t notice until your write performance nosedives. When I work with teams building time-series datasets or event logs, one of the most common pitfalls I see is sequential writes to a single partition. Timestamp as a partition key sounds intuitive (and easy), but here’s what actually happens: 🔹 Writes start hitting a narrow window of partitions (like t1–t2 in this example) 🔹 That partition becomes a hotspot, overloaded with inserts 🔹 Meanwhile, surrounding partitions (t0–t1, t2–t3) sit nearly idle 🔹 Performance drops, latency increases, and in some systems—throughput throttling or even write failures kick in This is why choosing the right clustering/partitioning strategy is so critical. A few things that’ve worked well for us: ✅ Add high-cardinality attributes (like user_id, region, device) to the partitioning scheme ✅ Randomize write distribution if real-time access isn’t required (e.g., hash bucketing) ✅ Use ingestion time or write time sparingly, only when access patterns make sense ✅ Monitor partition skew early and often—tools like system views and query plans help! Partitioning should balance read performance and write throughput. Optimizing for just one leads to trouble. If you're building on time-series data, don’t sleep on this. The write patterns you define today can make or break your infra six months from now. #dataengineering

You need to understand this to create the best SQL indexes. The order of the columns matters. At some point, you learn that creating indexes can help improve queries' performance. Nonclustered Indexes help a lot. This index contains a sorted list of key values pointing to the data rows.  But, which columns should go first? It's like an index in a book, helping to quickly locate the data without scanning the entire table. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼: Imagine you have an Orders table with the following columns: • OrderID (Primary Key) • CustomerID • OrderDate • TotalAmount • Status 𝗟𝗲𝘁'𝘀 𝘀𝗮𝘆 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱 𝘁𝗼: "Retrieve the OrderID, CustomerID, OrderDate, and TotalAmount for all orders where the CustomerID is 'CUST123' and the Status is 'Shipped'. Sort the results by OrderDate." 𝗗𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗶𝗻𝗴 𝘁𝗵𝗲 𝗜𝗻𝗱𝗲𝘅 𝗢𝗿𝗱𝗲𝗿 1. Selectivity: The first column in your index should be the one that filters down to the fewest rows (most selective). In our query, CustomerID is likely to be very selective. 2. Subsequent Columns: Columns used in filtering and sorting come next. In our case, Status is used for filtering, and OrderDate is used for sorting. 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗢𝗿𝗱𝗲𝗿? • CustomerID: By placing CustomerID first, the database can quickly locate all orders for CUST123. • Status: Next, the index filters down to only those orders that are 'Shipped.' • OrderDate: Finally, the index allows the database to order the results by OrderDate. Indexes can do much more optimizing, but choosing the order of columns for your index key is an essential first step!

What are the most common performance bugs developers encounter when using databases? I like this paper because it carefully studies what sorts of database performance problems real developers encounter in the real world. The authors analyze several popular open-source web applications (including OpenStreetMap and Gitlab) to see where database performance falters and how to fix it. Here’s what they found: - ORM-related inefficiencies are everywhere. This won’t be surprising to most experienced developers, but by hiding the underlying SQL, ORMs make it easy to write very slow code. Frequently, ORM-generated code performs unnecessary sorts or even full-table scans, or takes multiple queries to do the job of one. Lesson: Don’t blindly trust your ORM–for important queries, check if the SQL it generates makes sense. - Many queries are completely unnecessary. For example, many programs run the exact same database query in every iteration of a loop. Other programs load far too much data that they don’t need. These issues are exacerbated by ORMs, which don’t make it obvious that your code contains expensive database queries. Lesson: Look at where your queries are coming from, and see if everything they’re doing is necessary. - Figuring out whether data should be eagerly or lazily loaded is tricky. One common problem is loading data too lazily–loading 50 rows from A then for each loading 1 row from B (51 queries total) instead of loading 50 rows from A join B (one query total). But an equally common problem is loading data too eagerly–loading all of A, and also everything you can join A with, when in reality all the user wanted was the first 50 rows of A. Lesson: When designing a feature that retrieves a lot of data, retrieve critical data as efficiently as possible, but defer retrieving other data until needed. - Database schema design is critical for performance. The single most common and impactful performance problem identified is missing database indexes. Without an index, queries often have to do full table scans, which are ruinously slow. Another common problem is missing fields, where an application expensively recomputes a dependent value that could have just been stored as a database column. Lesson: Check that you have the right indexes. Then double-check. Interestingly, although these issues could cause massive performance degradation, they’re not too hard to fix–many can be fixed in just 1-5 lines of code, and few require rewriting more than a single function. The hard part is understanding what problems you have in the first place. If you know what your database is really doing, you can make it fast!

SQL Query Optimization Best Practices Optimizing SQL queries in SQL Server is crucial for improving performance and ensuring efficient use of database resources. Here are some best practices for SQL query optimization in SQL Server: 1). Use Indexes Wisely: a. Identify frequently used columns in WHERE, JOIN, and ORDER BY clauses and create appropriate indexes on those columns. b. Avoid over-indexing as it can degrade insert and update performance. c. Regularly monitor index usage and performance to ensure they are providing benefits. 2). Write Efficient Queries: a. Minimize the use of wildcard characters, especially at the beginning of LIKE patterns, as it prevents the use of indexes. b. Use EXISTS or IN instead of DISTINCT or GROUP BY when possible. c. Avoid using SELECT * and fetch only the necessary columns. d. Use UNION ALL instead of UNION if you don't need to remove duplicate rows, as it is faster. e. Use JOINs instead of subqueries for better performance. f. Avoid using scalar functions in WHERE clauses as they can prevent index usage. 3). Optimize Joins: a. Use INNER JOIN instead of OUTER JOIN if possible, as INNER JOIN typically performs better. b. Ensure that join columns are indexed for better join performance. c. Consider using table hints like (NOLOCK) if consistent reads are not required, but use them cautiously as they can lead to dirty reads. 4). Avoid Cursors and Loops: a. Use set-based operations instead of cursors or loops whenever possible. b. Cursors can be inefficient and lead to poor performance, especially with large datasets. 5). Use Query Execution Plan: a. Analyze query execution plans using tools like SQL Server Management Studio (SSMS) or SQL Server Profiler to identify bottlenecks and optimize queries accordingly. b. Look for missing indexes, expensive operators, and table scans in execution plans. 6). Update Statistics Regularly: a. Keep statistics up-to-date by regularly updating them using the UPDATE STATISTICS command or enabling the auto-update statistics feature. b. Updated statistics help the query optimizer make better decisions about query execution plans. 7. Avoid Nested Queries: a. Nested queries can be harder for the optimizer to optimize effectively. b. Consider rewriting them as JOINs or using CTEs (Common Table Expressions) if possible. 8. Partitioning: a. Consider partitioning large tables to improve query performance, especially for queries that access a subset of data based on specific criteria. 9. Use Stored Procedures: a. Encapsulate frequently executed queries in stored procedures to promote code reusability and optimize query execution plans. 10). Regular Monitoring and Tuning: a. Continuously monitor database performance using SQL Server tools or third-party monitoring solutions. b. Regularly review and tune queries based on performance metrics and user feedback.  #sqlserver #performancetuning #database #mssql

🔹 Optimizing Query Performance in Snowflake: Frustrated with slow query performance in your data warehouse? Optimizing query performance in Snowflake can significantly enhance your data processing speed and efficiency. Let’s dive into some proven techniques to make your queries run faster. 🚀 Imagine this: You’re running important analytics, but slow query performance is causing delays. Snowflake offers several features and best practices to optimize query performance and ensure you get your insights quickly. 🌟 Here are some tips to optimize query performance in Snowflake: 1. Use Clustering Keys: Define clustering keys to organize your data physically on disk. This helps Snowflake scan only the relevant data, speeding up query performance. 📊 2. Optimize Data Types: Choose appropriate data types for your columns. Using efficient data types can reduce storage space and improve query performance. 🔍 3. Minimize Data Movement: Reduce data movement by leveraging Snowflake’s ability to perform operations where the data resides. This minimizes the time spent on data transfer and boosts performance. 🏃♂️ 4. Leverage Result Caching: Enable result caching to reuse the results of previous queries. This can dramatically speed up query performance for repeated queries. 🗃️ 5. Use Materialized Views: Create materialized views for frequently queried data. Materialized views store the results of a query, allowing faster retrieval of data. 🛠️ 6. Partition and Cluster Properly: Properly partition and cluster your tables to ensure efficient data access and retrieval. This can significantly reduce query times. ⚡ 7. Monitor and Analyze Queries: Regularly monitor and analyze your query performance using Snowflake’s Query Profile tool. Identify and address slow-running queries to optimize performance. 📈 8. Optimize Joins: Use appropriate join types and ensure that your join conditions are properly indexed. This can reduce the time needed to execute join operations. 🔄 Why does this matter? Optimizing query performance ensures that your analytics run smoothly and efficiently, providing timely insights for better decision-making. It also helps in managing costs by reducing the compute resources required for processing. 💡 Pro Tip: Regularly review and update your query optimization strategies to keep up with changing data and workload patterns. How do you optimize query performance in your data warehouse? Have you tried any of these techniques in Snowflake? 💬 Share your thoughts or experiences in the comments below! 🚀 Ready to boost your query performance with Snowflake? Follow my profile for more insights on data engineering and cloud solutions: [https://lnkd.in/gVUn5_tx) #DataEngineering #Snowflake #DataWarehouse #CloudComputing #QueryOptimization #Performance

Enhancing SQL query efficiency is essential for improving database performance and ensuring swift data retrieval. 𝐇𝐞𝐫𝐞 𝐚𝐫𝐞 𝐬𝐨𝐦𝐞 𝐞𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥 𝐭𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬 𝐭𝐨 𝐠𝐞𝐭 𝐲𝐨𝐮 𝐬𝐭𝐚𝐫𝐭𝐞𝐝: 1. Use Appropriate Indexing 𝐖𝐡𝐚𝐭 𝐭𝐨 𝐝𝐨: Create indexes on columns frequently used in WHERE clauses, JOIN conditions, and ORDER BY clauses. 𝐑𝐞𝐚𝐬𝐨𝐧: Indexes provide quick access paths to the data, significantly reducing query execution time. 2. Limit the Columns in SELECT Statements 𝐖𝐡𝐚𝐭 𝐭𝐨 𝐝𝐨: Specify only the necessary columns in your SELECT statements. 𝐑𝐞𝐚𝐬𝐨𝐧: Fetching only required columns reduces data transfer from the database to the application, speeding up the query and reducing network load. 3. Avoid Using SELECT 𝐖𝐡𝐚𝐭 𝐭𝐨 𝐝𝐨: Explicitly list the columns you need in your SELECT statement instead of using SELECT *. 𝐑𝐞𝐚𝐬𝐨𝐧: SELECT retrieves all columns, leading to unnecessary I/O operations and processing of unneeded data. 4. Use WHERE Clauses to Filter Data 𝐖𝐡𝐚𝐭 𝐭𝐨 𝐝𝐨: Filter data as early as possible using WHERE clauses. 𝐑𝐞𝐚𝐬𝐨𝐧: Early filtering reduces the number of rows processed in subsequent operations, enhancing query performance by minimizing dataset size. 5. Optimize JOIN Operations 𝐖𝐡𝐚𝐭 𝐭𝐨 𝐝𝐨: Use the most efficient type of JOIN for your scenario and ensure that JOIN columns are indexed. 𝐑𝐞𝐚𝐬𝐨𝐧: Properly indexed JOIN columns significantly reduce the time required to combine tables. 6. Use Subqueries and CTEs Wisely 𝐖𝐡𝐚𝐭 𝐭𝐨 𝐝𝐨: Analyze the execution plan of subqueries and Common Table Expressions (CTEs) and consider alternatives if performance issues arise. 𝐑𝐞𝐚𝐬𝐨𝐧: While simplifying complex queries, subqueries and CTEs can sometimes degrade performance if not used correctly. 7. Avoid Complex Calculations and Functions in WHERE Clauses 𝐖𝐡𝐚𝐭 𝐭𝐨 𝐝𝐨: Perform calculations or use functions outside the WHERE clause or use indexed columns for filtering. 𝐑𝐞𝐚𝐬𝐨𝐧: Calculations or functions in WHERE clauses can prevent the use of indexes, leading to full table scans. 8. Use EXPLAIN Plan to Analyze Queries 𝐖𝐡𝐚𝐭 𝐭𝐨 𝐝𝐨: Regularly use the EXPLAIN command to understand how the database executes your queries. 𝐑𝐞𝐚𝐬𝐨𝐧: The execution plan provides insights into potential bottlenecks, allowing you to optimize queries effectively. 9. Optimize Data Types 𝐖𝐡𝐚𝐭 𝐭𝐨 𝐝𝐨: Choose the most appropriate data types for your columns, such as using integer types for numeric data instead of strings. 𝐑𝐞𝐚𝐬𝐨𝐧: Proper data types reduce storage requirements and improve query processing speed. What other techniques would you suggest? If you found this helpful, feel free to... 👍 React 💬 Comment ♻️ Share #databases #sql #data #queryoptimization #dataanalytics

What's the number one thing to think about when optimizing #SQL queries? You need to reduce the amount of data you process! This sounds obvious but I'll show you some of the things I think about when writing performant SQL queries. First step is to only SELECT what you need. If you only need a couple columns and your table has 100, don't do a SELECT *. Additionally, it's just best practice to be more explicit about what you're after. The next step is to apply filters to your data as early as possible. If your source data has a timestamp and you only care about today's data, make sure to apply a WHERE statement right away. You want to avoid a bunch of chained CTEs that do complex things without any filters, and then a filter in the final SELECT. Although optimizers are quite good, they aren't perfect. There are certain times queries / predicates are too complex to "push down". If you make it a habit to filter the data as soon as you can, you'll be able to avoid instances where the optimizer doesn't figure it out. Similarly, you should try to partition, cluster, or index your data on dimensions that are queried often and evenly splits your data. Different warehouses have different mechanisms but they all pretty much do the same thing. They allow you to skip processing data. For example, partitioning in Hive/Spark is basically like folders on your computer. If you partitioned your data by date, you'd have 365 folders for 1 year of data. Without partitioning, you'd have to look at every single file in every single folder to figure out if you need the data. With partitioning, you can simply look in the folders you care about. As you know I'm not the biggest fan of CTEs. But if you're using CTEs and reusing them multiple times, you should realize that every time you reference it, it's going to recompute, there's no automatic caching. So if you have an expensive CTE that gets used multiple times, it can be better to materialize it as a temporary table (or if you're using Postgres, use the materialized keyword). When you join data, you want to try to avoid shuffle joins. This happens when both sides are too big to fit in memory and must be sent to many different machines to join. If one side of your join is small and can fit in memory, an engine can perform a broadcast join. The small dataset will be copied to each machine and join, so much less data is processed or moved around! Finally, when joining data, you want to make sure that you're avoiding a many-many join if possible. This can happen if you have rows with the same keys on both side. When this happens, the number of resulting rows explodes, causing a ton data processing. So although it seems obvious to "process less data", there can be many factors that cause you to process more data than intended! #dataengineering

Are your SQL queries running as efficiently as they should? SQL performance tuning isn't just about making queries run faster—it's about optimizing resource usage, reducing load times, and improving overall database efficiency. Here are 15 SQL optimization techniques that can help you write high-performance queries: ✅ Use temporary tables – Simplify complex queries and improve readability. ✅ Apply WHERE clauses early – Filter data at the start to reduce unnecessary computations. ✅ Utilize GROUP BY wisely – Cluster similar data for better aggregation. ✅ Harness indexing – Speed up searches by indexing frequently queried columns. ✅ Prefer INNER JOIN over OUTER JOIN – Reduce the result set size when possible. ✅ Use EXISTS instead of IN/NOT IN – Faster performance for large datasets. ✅ **Avoid SELECT *** – Query only the columns you need. ✅ Use LIMIT/TOP – Restrict returned rows and prevent overloading the system. ✅ Leverage aggregate functions – Optimize SUM(), AVG(), and COUNT() for large datasets. ✅ Implement CASE statements – Handle conditional logic more efficiently. ✅ Use stored procedures – Minimize network traffic and improve execution speed. ✅ Be cautious with wildcard searches – Avoid using % at the start of LIKE queries. ✅ Choose UNION ALL over UNION – Reduce unnecessary sorting operations. ✅ Limit subquery usage – Consider JOINs or temporary tables instead. ✅ Use table aliases smartly – Keep your SQL readable and maintainable. Even minor SQL optimizations can lead to significant speed improvements and reduced database costs. Credits: Sai Kumar Bysani

Sick of SQL queries that take forever to execute? Speed up your queries by clustering your tables. Here's everything you need to know for clustering tables in BigQuery: - When you cluster a table, you are essentially pre-sorting the rows. This speeds up queries when you try to select a subset of rows that fit a particular condition. For example, if you clustered a table on "country" and then select all orders WHERE country = 'USA', your query will run faster than if the table weren't clustered. - When you filter columns on a clustered table using a WHERE statement, in order to get a performance boost, you need to filter the columns in the same order they were clustered. - Another requirement for getting the performance boost: you must always include at least the first clustered column in your filter conditions. If you don't include it, you won't get the performance boost. - You can cluster up to four (4) columns on every table in BigQuery. - Here is how you create a clustered table: CREATE OR REPLACE TABLE my_dataset.my_clustered_table AS ( SELECT * FROM my_dataset.not_clustered_table ) CLUSTER BY customer_id OPTIONS (description = 'A table clustered by customer_id'); #sql #bigquery