Haider Abbasi的动态

Spark Optimization Tip: If you’re working with massive datasets in Databricks, you know the pain of slow queries and resource-heavy jobs. But what if I told you that a single line of code could transform your workflow’s speed and efficiency? Enter cache – the secret ingredient to faster data processing. Here's why caching is a must-have in your Databricks toolkit: 1.Supercharged Speed: By caching frequently-used data in memory, you avoid the slow process of reading from disk repeatedly. This can make your Spark jobs 10x faster! ? 2.Lower Computation Costs: Faster queries mean lower cluster usage and reduced costs ?? 3.Enhanced User Experience: Caching makes data more readily available for analysis, especially helpful when you’re testing or building models in Databricks notebooks. Always remember to uncache data when you’re done with it to free up memory using unpersist(). #Databricks #DataEngineering #SparkOptimization #BigData #TechTips #DataScience

Good news for Data scientists and Engineers! The ?????? ???????????????? ?????????????? on Databricks notebooks is here to enhance our workflow. ?It's a game-changing tool that's set to elevate our approach to data analysis. ?? This innovative functionality enables us to dive deep into our code, identifying and resolving issues swiftly. Whether it's refining complex machine learning models or optimizing intricate data pipelines, the Databricks Debugger enhances efficiency and accuracy in our data-driven projects. Key benefits include: ?????????????????? ??????????????????: Pinpoint and resolve issues faster, minimizing downtime in data workflows. ?????????????????????? ????????????????????????: Fine-tune algorithms and data transformations with precision, ensuring optimal performance. ???????????????? ??????????????????????????: Facilitate seamless teamwork by providing clear insights into code execution and logic. Whether you're fine-tuning machine learning models or optimizing data transformations, the Databricks Debugger is designed to streamline your process and boost productivity. ?Learn more about the Databricks Debugger from here https://lnkd.in/g3neYsFE Follow Satish Kumar for more data engineering content #data #dataengineering #CloudStorage #Snowflake #databricks #datamanagement #lakehouse #deltalake #Snowflake #TechInnovation #Analytics #BigData #CloudComputing #DataManagement

?? What Can We Do with Databricks? ?? Databricks is a unified analytics platform built on Apache Spark, designed to empower Data Engineers, Analysts, and Scientists. Whether you're building data pipelines, analyzing data, or creating machine learning models, Databricks offers the tools and scalability you need to succeed. Here’s how Databricks can elevate your work: #DataEngineering #BigData #ETL #ApacheSpark #CloudComputing #DataAnalytics #DataVisualization #BusinessIntelligence #DataDriven #TechInnovation #DataScience #MachineLearning #PredictiveAnalytics #MLPipelines #ApacheSpark

When someone says they're optimizing code in Databricks... most of the time it's just for show! ?? Let me explain why. Spark itself is pretty smart—thanks to the Catalyst Optimizer and Spark SQL Engine, it already does a lot of heavy lifting for you. And since Spark 3.0, with Adaptive Query Execution (AQE) in play, even join strategies are optimized at runtime. You can tweak things like the broadcast threshold, which is typically done by admins when setting up Databricks clusters. The only real need for manual optimization nowadays? It's mostly for: - User-defined functions (UDFs): Spark can't optimize them, so we need to be careful here. - RDD operations: Those low-level RDDs... but let's be honest, who's doing that in Databricks anymore? - And of course, caching or persisting when needed. So, how do we optimize Spark jobs today? Honestly, it's more about letting Spark do its thing, and stepping in where it can't! But there are still some tricks to squeeze out performance, like: - Predicate and Projection Pushdown for less data read. - Choosing built-in functions over UDFs. - And of course, Parquet over CSV for better storage efficiency. These days, it’s all about using AQE smartly, applying some manual tweaks, and enjoying the performance boost without the hassle. #Databricks #SparkOptimization #ApacheSpark #AdaptiveQueryExecution #BigData #DataEngineering #DataOptimization #SparkTips #CloudComputing #DataScience #ETL #BigDataAnalytics

Ever wonder why your Databricks jobs performance changes over time? Worry no more, with our new job-level metrics timeline view! Now you can track Spark properties over time for each of your job runs! You can now answer questions like: 1) Why are my runtimes growing since last week? 2) Is my data size changing over the past month? 3) Is my job about to crash? 4) Did my total job cost change from last week? Many users asked us to plot these metrics so they can get a quick insight into what's changing with their production jobs from run to run. Try it out today, as this feature is now GA and is included "out of the box" with Gradientt! https://lnkd.in/gxH4eRcp #dataengineering #databricks Sync Computing

Databricks Day 3:?#dataengineeringin30days When you run a piece of code within a Spark application, a complex yet efficient sequence of events unfolds behind the scenes to ensure your data is processed accurately and swiftly. Here's an insider's view into the intricate journey that unfolds within Spark: Starting Point: SparkContext Creation Every Spark journey begins with the birth of a SparkContext. Think of this as the mastermind behind the operations, setting up the stage for the data adventure that lies ahead. It connects the dots between the application and the Spark environment. Initiating the Process: Job Submission The moment you trigger an action, Spark springs into action, breaking down your request into manageable chunks, outlining a game plan or, in Spark terms, submitting a job. Drafting the Blueprint: Logical Plan From your commands, Spark drafts a logical plan. It’s like plotting the route on a map without deciding the mode of transport – outlining what needs to be done but not delving into the specifics. Crafting the Strategy: Physical Plan and DAG Next, Spark’s Catalyst optimizer transforms the logical roadmap into a tangible strategy – the physical plan, visualized as a DAG (Directed Acyclic Graph). Here, Spark decides the nitty-gritty, plotting out each step of your data’s journey and optimizing the route for efficiency. Assigning the Tasks: Task Scheduling Like a seasoned general, Spark then divides the strategy into stages and dispatches tasks to its executor troops. It’s all about breaking down the big plan into actionable steps, ensuring every piece of data finds its rightful place. Into Action: Task Execution Now, the executors, Spark's foot soldiers, swing into action, processing your data in parallel, harnessing the full power of distributed computing. It’s where each executor performs its designated tasks diligently. The Finale: Job Wrap-up All insights are gathered, the SparkContext takes a bow and exits the stage, relinquishing the resources and marking the end of a data-processing saga. #gritsetgrow?#databricks?#azuredataengineering?#dataengineering

CRON as a scheduling opportunity in databricks CRON is a way of specifying when to run a job or a query in Databricks. It uses a special syntax that consists of six fields: seconds, minutes, hours, day of month, month, and day of week. For example,?0 0 10 * * ? means run at 10:00 every day. The first part is 0 seconds, the second part is 0 minutes, the third part is 10 hours, the fourth part is any day of the month, the fifth part is any month, and the sixth part is any day of the week. - To run a command every 15 minutes:?*/15 * * * * command - To run a command at 10:30 a.m. on the first Monday of every month:?30 10 1-7 * 1 command - To run a command every day at midnight, except on Sundays:?0 0 * * 1-6 command - To run a command every four hours, starting from 2 a.m.:?0 2/4 * * * command - To run a command on the last day of every month:?0 0 L * * command You can use CRON to schedule your Databricks workflows on a regular or continuous basis. To add a CRON schedule to your job or query, you can use the Databricks UI or the REST API. CRON is a powerful and flexible tool for scheduling your Databricks tasks. #databricks #data #dataengineering

Use #databricks delta.tuneFileSizesForRewrites table property. When this property is set to true, Databricks will automatically tune the file sizes based on workloads. For example, if you do a lot of merges on the Delta table, then the files will automatically be tuned to much smaller sizes than 1GB to accelerate the merge operation. Always explicitly broadcast smaller tables using hints or PySpark broadcast function Why do we need to explicitly broadcast smaller tables if AQE can automatically broadcast smaller tables for us? The reason for this is that AQE optimizes queries while they are being executed. Spark needs to shuffle the data on both sides and then only AQE can alter the physical plan based on the statistics of the shuffle stage and convert to broadcast join Therefore, if you explicitly broadcast smaller tables using hints, it skips the shuffle altogether and your job will not need to wait for AQE’s intervention to optimize the plan. Never broadcast a table bigger than 1GB because broadcast happens via the driver and a 1GB+ table will either cause OOM on the driver or make the drive unresponsive due to large GC pauses

Day 8/20?? Challenge to learn Databricks Yesterday we learned about SQL warehouses and clusters today we will be learning about Databricks Pool. ?? What are Databricks pools? ?? In Layman's terms, a Databricks pool is a pre-set group of computers that are always ready to run tasks, making it quicker and easier to get work done without setting up everything from scratch each time. ?? How are Databricks pools related to clusters and what sets them apart regarding their roles and availability? ?? In simple terms, a pool in Databricks is like a team of computers that are always on standby, ready to work. Conversely, a cluster is a group of computers that come together to perform specific tasks. Think of a pool as a group of specialized workers always available, while a cluster is a team assembled for a particular project. ?? Now what is auto-scaling in Databricks? ??In simple terms, Think of Autoscaling in Databricks like a manager for your team of clusters. It constantly monitors the workload and decides whether to add more clusters or reduce them based on how much work there is. So, it's directly related to clusters because it manages their numbers to ensure efficient task processing. ? Coming to our next topic Photon in Databricks ??In simple terms, Photon in Databricks is like a super-fast engine that helps process data lightning quickly. It's designed to speed up the performance of queries and computations, making everything run smoothly and efficiently. Just imagine it as the turbo boost for your data tasks! ?? Photon achieves its high-speed processing in Databricks: ?? Utilizes in-memory computing ?? Optimized data structures ?? Parallel processing ?? Resource optimization ?? High-speed computations ?? In a nutshell - Autoscaling is like the clever manager overseeing everything. - Photon is the super-fast engine that speeds up data processing. - Clusters are the temporary project teams brought in to handle tasks. - Pools are the standby workers ready to jump in when needed. Hope you enjoyed today's discussion, and I hope my simplified explanation of Databricks made sense. Let's catch up tomorrow with new topics. Take care till then! #Databricks #Photon #Autoscaling #Pools #DataEngineering #DataAnalyst

Databricks optimization techniques: 1) Use Delta Lake: Run OPTIMIZE to compact files, and use Z-Ordering for faster filtering. 2) Leverage Caching: Use cache() or Delta Caching for frequently accessed data. 3) Adjust Spark Configurations: Tune spark.sql.shuffle.partitions and executor settings for efficient resource usage. 4) Minimize Shuffling: Use Broadcast joins for small tables and partition on join keys. 5) Right Cluster Type: Use interactive clusters for development, job clusters for production, and auto-scaling to save costs. 6) Optimize File Management: Compact small files in Delta Lake to avoid performance lags, especially in streaming jobs. 7) Enable AQE: Adaptive Query Execution optimizes joins, partitions, and data skew at runtime. 8) Efficient Serialization: Use Kryo for faster and more efficient object serialization. 9) SQL Analytics: Optimize SQL queries using indexes, partition pruning, and avoiding complex subqueries. 10) Manage Memory: Fine-tune memory and garbage collection settings for heavy data workloads. These steps help speed up data processing, reduce costs, and ensure efficient resource usage in Databricks. #Databricks #Databricksoptimizationtechniques #Optimizationinpyspark #dataengineerinterviewquestion #Azuredataengineer #Pyspark #CloudComputing #PerformanceOptimization #ApacheSpark #DataPipeline #PerformanceTuning #BigData #DeltaLake #DataAnalytics #ETL #softwareengineering #technicalinterview #Optimization #Spark