Diagnosing Sluggish Snowflake Question Efficiency


As a result of Rockset helps organizations obtain the information freshness and question speeds wanted for real-time analytics, we generally are requested about approaches to bettering question velocity in databases usually, and in standard databases reminiscent of Snowflake, MongoDB, DynamoDB, MySQL and others. We flip to business consultants to get their insights and we go on their suggestions. On this case, the collection of two posts that comply with deal with enhance question velocity in Snowflake.

Each developer needs peak efficiency from their software program providers. In relation to Snowflake efficiency points, you’ll have determined that the occasional gradual question is simply one thing that you need to reside with, proper? Or perhaps not. On this publish we’ll talk about why Snowflake queries are gradual and choices you need to obtain higher Snowflake question efficiency.

It’s not all the time simple to inform why your Snowflake queries are working slowly, however earlier than you’ll be able to repair the issue, you need to know what’s taking place. Partially one in every of this two-part collection, we’ll show you how to diagnose why your Snowflake queries are executing slower than standard. In our second article, What Do I Do When My Snowflake Question Is Sluggish? Half 2: Options, we take a look at the perfect choices for bettering Snowflake question efficiency.

Diagnosing Queries in Snowflake

First, let’s unmask frequent misconceptions of why Snowflake queries are gradual. Your {hardware} and working system (OS) don’t play a job in execution velocity as a result of Snowflake runs as a cloud service.

The community might be one motive for gradual queries, but it surely’s not important sufficient to gradual execution on a regular basis. So, let’s dive into the opposite causes your queries may be lagging.

Verify the Info Schema

In brief, the INFORMATION_SCHEMA is the blueprint for each database you create in Snowflake. It lets you view historic knowledge on tables, warehouses, permissions, and queries.

You can not manipulate its knowledge as it’s read-only. Among the many principal features within the INFORMATION_SCHEMA, you’ll discover the QUERY_HISTORY and QUERY_HISTORY_BY_* tables. These tables assist uncover the causes of gradual Snowflake queries. You may see each of those tables in use under.

Remember that this instrument solely returns knowledge to which your Snowflake account has entry.

Verify the Question Historical past Web page

Snowflake’s question historical past web page retrieves columns with precious info. In our case, we get the next columns:

  • EXECUTION_STATUS shows the state of the question, whether or not it’s working, queued, blocked, or success.
  • QUEUED_PROVISIONING_TIME shows the time spent ready for the allocation of an appropriate warehouse.
  • QUEUED_REPAIR_TIME shows the time it takes to restore the warehouse.
  • QUEUED_OVERLOAD_TIME shows the time spent whereas an ongoing question is overloading the warehouse.

Overloading is the extra frequent phenomenon, and QUEUED_OVERLOAD_TIME serves as a vital diagnosing issue.

Here’s a pattern question:

      choose *
      from desk(information_schema.query_history_by_session())
      order by start_time;

This offers you the final 100 queries that Snowflake executed within the present session. You can even get the question historical past based mostly on the consumer and the warehouse as effectively.

Verify the Question Profile

Within the earlier part, we noticed what occurs when a number of queries are affected collectively. It’s equally essential to handle the person queries. For that, use the question profile choice.

You’ll find a question’s profile on Snowflake’s Historical past tab.


The question profile interface appears to be like like a sophisticated flowchart with step-by-step question execution. You need to focus primarily on the operator tree and nodes.


The operator nodes are unfold out based mostly on their execution time. Any operation that consumed over one % of the full execution time seems within the operator tree.

The pane on the suitable facet exhibits the question’s execution time and attributes. From there, you’ll be able to work out which step took an excessive amount of time and slowed the question.

Verify Your Caching

To execute a question and fetch the outcomes, it would take 500 milliseconds. For those who use that question often to fetch the identical outcomes, Snowflake offers you the choice to cache it so the following time it’s sooner than 500 milliseconds.

Snowflake caches knowledge within the outcome cache. When it wants knowledge, it checks the outcome cache first. If it doesn’t discover knowledge, it checks the native laborious drive. If it nonetheless doesn’t discover the information, it checks the distant storage.

Retrieving knowledge from the outcome cache is quicker than from the laborious drive or distant reminiscence. So, it’s best follow to make use of the outcome cache successfully. Information stays within the outcome cache for twenty-four hours. After that, you need to execute the question once more to get the information from the laborious disk.

You possibly can take a look at how successfully Snowflake used the outcome cache. When you execute the question utilizing Snowflake, verify the Question Profile tab.

You learn how a lot Snowflake used the cache on a tab like this.


Verify Snowflake Be a part of Efficiency

For those who expertise slowdowns throughout question execution, you need to examine the anticipated output to the precise outcome. You may have encountered a row explosion.

A row explosion is a question outcome that returns way more rows than anticipated. Due to this fact, it takes way more time than anticipated. For instance, you would possibly count on an output of 4 million information, however the end result might be exponentially larger. This drawback happens with joins in your queries that mix rows from a number of tables. The be a part of order issues. You are able to do two issues: search for the be a part of situation you used, or use Snowflake’s optimizer to see the be a part of order.

A simple approach to decide whether or not that is the issue is to verify the question profile for be a part of operators that show extra rows within the output than within the enter hyperlinks. To keep away from a row explosion, make sure the question outcome doesn’t comprise extra rows than all its inputs mixed.

Just like the question sample, utilizing joins is within the palms of the developer. One factor is evident — unhealthy joins lead to gradual Snowflake be a part of efficiency, and gradual queries.

Verify for Disk Spilling

Accessing knowledge from a distant drive consumes extra time than accessing it from an area drive or the outcome cache. However, when question outcomes don’t match on the native laborious drive, Snowflake should use distant storage.

When knowledge strikes to a distant laborious drive, we name it disk spilling. Disk spilling is a typical reason behind gradual queries. You possibly can determine situations of disk spilling on the Question Profile tab. Check out “Bytes spilled to native storage.”


On this instance, the execution time is over eight minutes, out of which solely two % was for the native disk IO. Meaning Snowflake didn’t entry the native disk to fetch knowledge.

Verify Queuing

The warehouse could also be busy executing different queries. Snowflake can not begin incoming queries till ample assets are free. In Snowflake, we name this queuing.

Queries are queued in order to not compromise Snowflake question efficiency. Queuing might occur as a result of:

  • The warehouse you’re utilizing is overloaded.
  • Queries in line are consuming the mandatory computing assets.
  • Queries occupy all of the cores within the warehouse.

You possibly can depend on the queue overload time as a transparent indicator. To verify this, take a look at the question historical past by executing the question under.

      [ SESSION_ID => <constant_expr> ]
      [, END_TIME_RANGE_START => <constant_expr> ]
      [, END_TIME_RANGE_END => <constant_expr> ]
      [, RESULT_LIMIT => <num> ] )

You possibly can decide how lengthy a question ought to sit within the queue earlier than Snowflake aborts it. To find out how lengthy a question ought to stay in line earlier than aborting it, set the worth of the STATEMENT_QUEUED_TIMEOUT_IN_SECONDS column. The default is zero, and it may well take any quantity.

Analyze the Warehouse Load Chart

Snowflake affords charts to learn and interpret knowledge. The warehouse load chart is a helpful instrument, however you want the MONITOR privilege to view it.


Right here is an instance chart for the previous 14 days. If you hover over the bars, you discover two statistics:

  • Load from working queries — from the queries which are executing
  • Load from queued queries — from all of the queries ready within the warehouse

The overall warehouse load is the sum of the working load and the queued load. When there isn’t any rivalry for assets, this sum is one. The extra the queued load, the longer it takes to your question to execute. Snowflake might have optimized the question, however it might take some time to execute as a result of a number of different queries had been forward of it within the queue.

Use the Warehouse Load Historical past

You’ll find knowledge on warehouse hundreds utilizing the WAREHOUSE_LOAD_HISTORY question.

Three parameters assist diagnose gradual queries:

  • AVG_RUNNING — the common variety of queries executing
  • AVG_QUEUED_LOAD — the common variety of queries queued as a result of the warehouse is overloaded
  • AVG_QUEUED_PROVISIONING — the common variety of queries queued as a result of Snowflake is provisioning the warehouse

This question retrieves the load historical past of your warehouse for the previous hour:

  use warehouse mywarehouse;

      choose *

Use the Most Concurrency Degree

Each Snowflake warehouse has a restricted quantity of computing energy. Normally, the bigger (and dearer) your Snowflake plan, the extra computing horsepower it has.

A Snowflake warehouse’s MAX_CONCURRENCY_LEVEL setting determines what number of queries are allowed to run in parallel. Normally, the extra queries working concurrently, the slower every of them. But when your warehouse’s concurrency stage is just too low, it would trigger the notion that queries are gradual.

If there are queries that Snowflake cannot instantly execute as a result of there are too many concurrent queries working, they find yourself within the question queue to attend their flip. If a question stays within the line for a very long time, the consumer who ran the question might imagine the question itself is gradual. And if a question stays queued for too lengthy, it might be aborted earlier than it even executes.

Subsequent Steps for Bettering Snowflake Question Efficiency

Your Snowflake question might run slowly for varied causes. Caching is efficient however doesn’t occur for all of your queries. Verify your joins, verify for disk spilling, and verify to see in case your queries are spending time caught within the question queue.

When investigating gradual Snowflake question efficiency, the question historical past web page, warehouse loading chart, and question profile all supply precious knowledge, supplying you with perception into what’s going on.

Now that you just perceive why your Snowflake question efficiency is probably not all that you really want it to be, you’ll be able to slender down attainable culprits. The next move is to get your palms soiled and repair them.

Do not miss the second a part of this collection, What Do I Do When My Snowflake Question Is Sluggish? Half 2: Options, for tips about optimizing your Snowflake queries and different selections you can also make if real-time question efficiency is a precedence for you.

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