You're facing sluggish data warehouse queries. How can you uncover the elusive issue?
Facing sluggish data warehouse queries can be frustrating, but pinpointing the issue is possible with a few strategic steps.
When your data warehouse queries slow down, identifying the cause can feel like finding a needle in a haystack. Here’s how to troubleshoot effectively:
What strategies have you found helpful for speeding up sluggish queries?
You're facing sluggish data warehouse queries. How can you uncover the elusive issue?
Facing sluggish data warehouse queries can be frustrating, but pinpointing the issue is possible with a few strategic steps.
When your data warehouse queries slow down, identifying the cause can feel like finding a needle in a haystack. Here’s how to troubleshoot effectively:
What strategies have you found helpful for speeding up sluggish queries?
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SQL queries may take a long time to process if the database's CPU Utilization is high Common causes for high CPU Utilization: Queries which lack indexes Outdated index statistics used by data warehouses Query inefficiencies Workload increase Use AWS CloudWatch to monitor CPU Utilization of AWS RDS Monitor warehouse load in Snowflake Identify spikes in Snowflake and use History to list out the heavy queries. Send them for Holistics support EXPLAIN ANALYZE on a SQL query will reveal the query plan of that query Go to Job Monitoring Dashboard, find job logs of slow-running jobs Copy the generated SQL, append EXPLAIN ANALYZE to it Run whole query in DB Engine. If you are unsure on how to interpret the result, send it to Holistics
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1. Check if Data Model / DW-DB design is optimal for DW-BI queries For Row Level, On-Premise DW's - Dimensional Modeling with Proper Partitioning is preferred For Columnar & Cloud DW-DB's like Redshift & BigQuery - Sort keys, Partitioning Keys, Clustering need to be looked at 2. Query only the columns that are needed & cache the resultant 3. Split data across different nodes/slots/clusters wherein each node has its own dedicated compute & memory and can work on subset of the data, the resultant of each node/slot/cluster can then be combined to give the final output. 4. Size of data movement between nodes, dedicated network bandwidth available for data movement, 5. Type of storage 6. Data Virtualization for querying multiple varied DB's
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For start, I like this new format - where there is one long answer to this - as opposed to 7 specific things that can be a "catch all" for many questions. First off, "identifying the problem" very clearly is half the battle. Then, A *BIG* point is what is the platform that has this issue? Your on-premise DW has one way of triage - mostly mentioned here - versus Snowflake or Cloud DW has another. These vendors have very specific guidelines - sometimes very simple like give more resources in cloud. For on-prem, another issue is if this is a scale factor/ i.e. If a single query runs fine but when many run it at the same time that it slows?
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1-Monitoring the memory status, processing and disk speed in the operating system and database disk layer 2. Reviewing the objects used in the desired query and response time and resource consumption. 3. Reviewing the query execution plan in the database and identifying the best execution plan according to the database structure and resources. 4. Reviewing existing indexes and making necessary changes in creating or deleting table indexes or creating partition tables and coordinating with the software production unit. 5. Using appropriate tools to accelerate the relevant activity in times of crisis. 6. Reviewing the executed query and suggesting necessary changes to the software production unit
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Some of the major causes of sluggish queries are: Full table scans without inadequate filtering. Queries should be peer reviewed by Subject matter experts particularly before production rollouts and then profiled for performance post rollout. Data returned by the query results also have a big impact, so understanding the underlying data is also important. One way to avoid performance issues due to mixed workloads is to decouple analytical workloads from operational workloads.
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Common causes: - you've blundered a join - the keys in that joined table aren't actually unique - you're not using temporary tables for intermediate results in long and complex transforms - your process is just computationally heavy and you need to up the compute - the source tables aren't designed properly If you don't have good data governance then you need to thoroughly investigate all the data going in and validate that it meets your assumptions. If you do have good data governance, then you should evaluate the query plan to see where the bottleneck is occurring and why. It might be valid or it might be user error.
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Some ideas to start: - Identify the bottlenecks - Check base tableas (believe or not, I've found tables using the wron data type, wasting space and slowing down queries) . Most used are EXPLAIN o EXPLAIN ANALYZE and manual tables / queries reviews - SHOW PROCESSLIST could check connection status and current activity - Check filters in queries. Check sub-queries - Deep analysis of indexes. Check the careful use of indexes on frequently used columns in the WHERE, JOIN, and ORDER BY clauses. - Ckeck if horizontal o vertical partitions are available for large ammount of data. - If possible, limit the amount of rows returned back - Keep an eye on SELECT DISTINCT clauses
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Review query plans to check for poorly formulated queries and missing indexes. Check for fragmentation of heap tables. Look at maintenance routines for the proper periodic rebuilding of indexes and updating of statistics based on the rate of change in your database. Check your locking statistics, and use read uncommitted or "with (no lock)" query hints where/if appropriate. Finally look at hardware resources to see if more RAM, faster disk, or more CPU cores can help.
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