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Category: Indexing

Finding Indexing Metrics in Cosmos DB

Hasan Savran looks at the numbers:

You might need Composite Indexes to make your queries more efficient, Cosmos DB does not create any Composite Indexes for you. You need to figure out which properties should have composite indexes then you need to change the indexing policy file to create them. 

    Indexing Metrics comes to your help when you need help with indexing policy. It tells you which indexes the current query uses and it gives you hints about what other indexes you should create to make the query work faster/cheaper. Like many other features of Cosmos DB, you need to write code by using SDK to see Indexing Metrics. The following example shows how to enable Indexing Metrics for your queries.

Click through for a code sample which shows how to collect index metrics.

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An Index for Change Tracking Cleanup

James Ferebee creates an index:

If the issue persists and we stack a lot of data that needs to be removed which can be too much for autocleanup to manage. At that point, we recommend running (as is indicated in the error message) sp_flush_CT_internal_table_on_demand which is discusseed in detailed in Amit’s blog post Change Tracking Cleanup–Part 1.

I will not discuss the internals of the process as Amit Banerjee already touched on this in the referenced blog. However if you are routinely encountering issues where autocleanup can’t keep up and/or manual cleanup is taking significant time, you can add the index and see if it helps and I have the process enumerated below. Keep in mind this is not guaranteed to fix all cleanup issues and it may still be necessary to run manual cleanup regularly. If you continue to have issues with cleanup feel free to create a ticket with us here at CSS to assist you and get specific data to your environment.

Read on for the index definition as well as some important notes about whether you might need it.

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Optimizing Index Spools

Francisco looks at index spools:

When we are analyzing execution plans, we may come across different types of Spool operators – Table Spools, Row Count Spools, Window Spools or Index Spools – that the Query Optimizer chooses for specific purposes. In this post we are going to briefly look into the Index Spool, how it can sometimes lead to suboptimal query performance, and what can be done to easily fix it.

My favorite description of this is Erik Darling’s: spools are SQL Server’s passive-aggressive way of telling you “I’m not saying you need an index but you need an index.”

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Consolidating Indexes

Erik Darling runs through an exercise:

The more columns you have in a table, the more potential column combinations there are for indexes. Much like columns, indexes tend to get added following the path of least resistance.

Very rarely does someone consider current indexes when deciding to add an index.

Erik’s process is a good one. The real pain comes when there are 40-50 indexes on a table (seriously…) and there are a lot of similar-but-not-quite-similar-enough indexes.

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The Practical Costs of Index Fragmentation

Tibor Karaszi digs into index performance:

See numbers and diagrams at the end, or at the top. I measured a few cases: the difference between no external fragmentation and severe external fragmentation (over 99%). I have both a narrow index and a wide index, and I read one (1), 10,000 and 100,000 rows using index searches (“range scan”). There were obviously no difference reading 1 row so I exclude that from my discussion below. For the other cases the extra time with an extreme level of external fragmentation is (from lowest impact to highest) 7%, 10%, 13% and 32%. The highest number (32%) is when reading many rows from a narrow index, i.e. many rows per page. Again, this is with an extreme level of fragmentation.

What’s interesting is that for the most part, there’s a negligible difference between ~0% internal fragmentation and ~99% internal fragmentation. The follow-on question is, how much are defrag operations costing you in performance and when is the benefit worth the cost?

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The Upsert Pattern and Unique Indexes in SQL Server

Michael J Swart recommends a unique index:

To avoid deadlocks when implementing the upsert pattern, make sure the index on the key column is unique. It’s not sufficient that all the values in that particular column happen to be unique. The index must be defined to be unique, otherwise concurrent queries can still produce deadlocks.

Read on for Michael’s thoughts as well as some smart commenters adding on.

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Tuning Non-Clustered Indexes via Missing Index Suggestions

Kendra Little announces a new article:

We’ve just published a new article in the SQL docs, Tune nonclustered indexes with missing index suggestions . The article explains what the missing index feature is, limitations of the feature, and how to use missing index DMVs and missing index suggestions in Query Store to tune indexes.

Read on to understand the impetus behind this article and then go check it out yourself.

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Understanding Plans: Seeks and Scans

Erik Darling made me rhyme. First up, data retrieval via seek:

People. People complain. People complain about SQL Server. That’s probably why I get paid to deal with it, but whatever.

One complaint I get to hear week in and week out is that SQL Server isn’t using someone’s index, or that there are too many index scans and they’re slow.

That might actually be a composite of like twelve complaints, but let’s not start counting.

Erik gives us three reasons why we might not see a seek. But wait, there’s more!

I’m not sure why scans got such a bad rap. Perhaps it’s a leftover from the Bad Old Days© when people worried about logical fragmentation and page splits.

What I mean to say is: scans are often treated with a level of revulsion and focus that distracts people from larger issues, and is often due to some malpractice on their part.

This is true–scans aren’t inherently bad and Erik gives us a better mental model to work with.

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Index Design Guide Updates

Kendra Little has a guide for us:

We’ve recently updated the SQL Server and Azure SQL index architecture and design guide. This article is an in-depth guide to indexing in databases using the SQL Server engine, including SQL Server, Azure SQL Database, Azure SQL Managed Instance, and Azure Synapse Analytics.

Our recent update adds a table to categorize the types of indexes discussed in the article, clarifies B-trees vs B+ trees, and describes how row locators (aka “secret columns”) are used in nonclustered indexes.

Read on for more information and do check out the guide.

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