HomeBig DataDiagnosing Gradual Snowflake Question Efficiency

Diagnosing Gradual Snowflake Question Efficiency

As a result of Rockset helps organizations obtain the info freshness and question speeds wanted for real-time analytics, we generally are requested about approaches to bettering question velocity in databases typically, and in well-liked databases akin to Snowflake, MongoDB, DynamoDB, MySQL and others. We flip to trade specialists to get their insights and we go on their suggestions. On this case, the collection of two posts that comply with tackle how you can enhance question velocity in Snowflake.

Each developer desires peak efficiency from their software program providers. In terms of Snowflake efficiency points, you could have determined that the occasional gradual question is simply one thing that you must dwell with, proper? Or possibly not. On this put up we’ll focus on why Snowflake queries are gradual and choices you must obtain higher Snowflake question efficiency.

It’s not at all times straightforward to inform why your Snowflake queries are working slowly, however earlier than you possibly can repair the issue, you must know what’s occurring. Partially one in every of this two-part collection, we’ll enable you diagnose why your Snowflake queries are executing slower than traditional. In our second article, What Do I Do When My Snowflake Question Is Gradual? Half 2: Options, we take a look at one of the best 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 task in execution velocity as a result of Snowflake runs as a cloud service.

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

Verify the Data Schema

In brief, the INFORMATION_SCHEMA is the blueprint for each database you create in Snowflake. It permits you to 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 will see that the QUERY_HISTORY and QUERY_HISTORY_BY_* tables. These tables assist uncover the causes of gradual Snowflake queries. You will see each of those tables in use under.

Understand that this device 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 invaluable data. 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 acceptable 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 an important diagnosing issue.

Here’s a pattern question:

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

This provides you the final 100 queries that Snowflake executed within the present session. You may also get the question historical past based mostly on the person and the warehouse as properly.

Verify the Question Profile

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

Yow will discover a question’s profile on Snowflake’s Historical past tab.


The question profile interface seems to be like a sophisticated flowchart with step-by-step question execution. It is best 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 aspect exhibits the question’s execution time and attributes. From there, you possibly can 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. Should you use that question regularly to fetch the identical outcomes, Snowflake offers you the choice to cache it so the subsequent time it’s quicker than 500 milliseconds.

Snowflake caches knowledge within the consequence cache. When it wants knowledge, it checks the consequence cache first. If it doesn’t discover knowledge, it checks the native onerous drive. If it nonetheless doesn’t discover the info, it checks the distant storage.

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

You possibly can try how successfully Snowflake used the consequence cache. When you execute the question utilizing Snowflake, examine the Question Profile tab.

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


Verify Snowflake Be a part of Efficiency

Should you expertise slowdowns throughout question execution, you need to evaluate the anticipated output to the precise consequence. You might have encountered a row explosion.

A row explosion is a question consequence that returns much more rows than anticipated. Due to this fact, it takes much more time than anticipated. For instance, you may anticipate an output of 4 million information, however the consequence could possibly be exponentially larger. This downside 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 straightforward technique to decide whether or not that is the issue is to examine 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 consequence doesn’t comprise extra rows than all its inputs mixed.

Much like the question sample, utilizing joins is within the palms of the developer. One factor is obvious — dangerous joins end in 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 a neighborhood drive or the consequence cache. However, when question outcomes don’t match on the native onerous drive, Snowflake should use distant storage.

When knowledge strikes to a distant onerous drive, we name it disk spilling. Disk spilling is a standard explanation for 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 enough sources are free. In Snowflake, we name this queuing.

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

  • The warehouse you might be utilizing is overloaded.
  • Queries in line are consuming the required computing sources.
  • Queries occupy all of the cores within the warehouse.

You possibly can depend on the queue overload time as a transparent indicator. To examine 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 will possibly take any quantity.

Analyze the Warehouse Load Chart

Snowflake provides charts to learn and interpret knowledge. The warehouse load chart is a useful device, 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 competition for sources, this sum is one. The extra the queued load, the longer it takes in your question to execute. Snowflake could 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

Yow will discover knowledge on warehouse masses utilizing the WAREHOUSE_LOAD_HISTORY question.

Three parameters assist diagnose gradual queries:

  • AVG_RUNNING — the typical variety of queries executing
  • AVG_QUEUED_LOAD — the typical variety of queries queued as a result of the warehouse is overloaded
  • AVG_QUEUED_PROVISIONING — the typical 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 Stage

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 degree 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 person 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 Enhancing Snowflake Question Efficiency

Your Snowflake question could run slowly for varied causes. Caching is efficient however doesn’t occur for all of your queries. Verify your joins, examine for disk spilling, and examine 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 invaluable knowledge, providing you with perception into what’s going on.

Now that you just perceive why your Snowflake question efficiency will not be all that you really want it to be, you possibly can slender down doable 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 Gradual? Half 2: Options, for recommendations on optimizing your Snowflake queries and different decisions you can also make if real-time question efficiency is a precedence for you.

Rockset is the real-time analytics database within the cloud for contemporary knowledge groups. Get quicker analytics on more energizing knowledge, at decrease prices, by exploiting indexing over brute-force scanning.



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