for Data Enthusiasts
Snowflake’s cloud-native architecture makes it incredibly easy to get started — but running it efficiently at scale is a whole different game. If you’ve ever faced slow queries, ballooning credit consumption, or unpredictable performance, you’re not alone. Tuning Snowflake workloads requires more than just adjusting warehouse sizes — it involves understanding how Snowflake stores data, … Read more
Apache Spark has revolutionized the way we process large-scale data — delivering unparalleled speed, scalability, and flexibility. But as many engineers discover, achieving optimal performance in Spark is far from automatic. Your job runs — but takes longer than expected. The cluster scales — but the costs rise disproportionately. Memory errors appear out of nowhere. … Read more
Observations : Count(1) or Count(*) – This is never expanded on each column individually so will work perfectly fine on complete data. Count(1) is more optimized then Count(*) Count(source.*) – source represents “Left table” of “Left Outer Join”: This will be evaluated as Count(source.col1, source.col2, …. source.colN ) So, if any column has NULL, then the complete row … Read more
I was doing some cost comparison of using CSV files vs Parquet File. Interestingly, when using Parquet format, data scanning for similar queries, cost 99% less as compared to CSV format. Queries ( Mentioned only for Parquet) CSV ( 11.32 GB )Run Time (in sec) CSV ( 11.32 GB )DataScanned (in GB) PARQUET ( 4.1 GB )Run Time (in sec) PARQUET ( 4.1 GB )DataScanned (in GB) … Read more
Below query seems like the simplest way to create a replica of table. But simplicity comes with some cost as well. Above query will : NOT create partitions if there are any on TABLE_NAME_2 run very slow Instead of above we should follow following 2 way approach : CREATE TABLE TABLE_NAME Like TABLE_NAME_2; — … Read more
If you have a table which is partitioned on a column then doingCompute stats TABLE_NAMEwill execute on all partitions. Internally compute stats run NDV function on each column to get numbers. However NDV function works faster then other count(COLUMN), but it will run for each partition which may be irrelevant when you are working/updating/modifying values … Read more
From 40 minutes to just 4 minutes Impala to_utc_timestamp() function is used to convert date/timestamp timezone to UTC. But it works very slow. If you have less data in table even then you can easily notice its slow performance. I faced a similar issue and noticed it was taking around 40 minutes alone to complete … Read more
We all know that to optimise our queries these 3 strategies are like most common : Partitioned table Bucketing Collecting Stats But sometimes a simple query will run on ALL partitions instead of one. You may notice your query should work on one partition but it will run on all partitions.Let me show you an … Read more