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Upgrade to Microsoft Edge to take advantage of the latest features, security updates, and technical support. Apply these recommendations to get results faster and avoid timeouts while running complex queries. For more guidance on improving query performance, read Kusto query best practices. Depending on its size, each tenant has access to a set amount of CPU resources allocated for running advanced hunting queries. For detailed information about various usage parameters, read about advanced hunting quotas and usage parameters. After running your query, you can see the execution time and its resource usage Low, Medium, High. High indicates that the query took more resources to run and could be improved to return results more efficiently. Customers who run multiple queries regularly should track consumption and apply the optimization guidance in this article to minimize disruption resulting from exceeding quotas or usage parameters. Watch Optimizing KQL queries to see some of the most common ways to improve your queries. Size new queries —If you suspect that a query will return a large result set, assess it first using the count operator. Use limit or its synonym take to avoid large result sets. In the example below, the parsing function extractjson is used after filtering operators have reduced the number of records. Has beats contains —To avoid searching substrings within words unnecessarily, use the has operator instead of contains. Learn about string operators. Look in specific columns —Look in a specific column rather than running full text searches across all columns. Case-sensitive for speed —Case-sensitive searches are more specific and generally more performant. Avoid the matches regex string operator or the extract function , both of which use regular expression. Reserve the use of regular expression for more complex scenarios. Read more about parsing functions. Filter tables not expressions —Don't filter on a calculated column if you can filter on a table column. No three-character terms —Avoid comparing or filtering using terms with three characters or fewer. These terms are not indexed and matching them will require more resources. Project selectively —Make your results easier to understand by projecting only the columns you need. Projecting specific columns prior to running join or similar operations also helps improve performance. The join operator merges rows from two tables by matching values in specified columns. Apply these tips to optimize queries that use this operator. Smaller table to your left —The join operator matches records in the table on the left side of your join statement to records on the right. By having the smaller table on the left, fewer records will need to be matched, thus speeding up the query. Use the inner-join flavor —The default join flavor or the innerunique-join deduplicates rows in the left table by the join key before returning a row for each match to the right table. If the left table has multiple rows with the same value for the join key, those rows will be deduplicated to leave a single random row for each unique value. This default behavior can leave out important information from the left table that can provide useful insight. For example, the query below will only show one email containing a particular attachment, even if that same attachment was sent using multiple emails messages:. Join records from a time window —When investigating security events, analysts look for related events that occur around the same time period. Applying the same approach when using join also benefits performance by reducing the number of records to check. Apply time filters on both sides —Even if you're not investigating a specific time window, applying time filters on both the left and right tables can reduce the number of records to check and improve join performance. Use hints for performance —Use hints with the join operator to instruct the backend to distribute load when running resource-intensive operations. Learn more about join hints. For example, the shuffle hint helps improve query performance when joining tables using a key with high cardinality—a key with many unique values—such as the AccountObjectId in the query below:. The broadcast hint helps when the left table is small up to , records and the right table is extremely large. For example, the query below is trying to join a few emails that have specific subjects with all messages containing links in the EmailUrlInfo table:. The summarize operator aggregates the contents of a table. Find distinct values —In general, use summarize to find distinct values that can be repetitive. It can be unnecessary to use it to aggregate columns that don't have repetitive values. While a single email can be part of multiple events, the example below is not an efficient use of summarize because a network message ID for an individual email always comes with a unique sender address. The summarize operator can be easily replaced with project , yielding potentially the same results while consuming fewer resources:. The following example is a more efficient use of summarize because there can be multiple distinct instances of a sender address sending email to the same recipient address. Such combinations are less distinct and are likely to have duplicates. Shuffle the query —While summarize is best used in columns with repetitive values, the same columns can also have high cardinality or large numbers of unique values. Like the join operator, you can also apply the shuffle hint with summarize to distribute processing load and potentially improve performance when operating on columns with high cardinality. The query below uses summarize to count distinct recipient email address, which can run in the hundreds of thousands in large organizations. To improve performance, it incorporates hint. On their own, they can't serve as unique identifiers for specific processes. To get a unique identifier for a process on a specific machine, use the process ID together with the process creation time. The following example query finds processes that access more than 10 IP addresses over port SMB , possibly scanning for file shares. The query summarizes by both InitiatingProcessId and InitiatingProcessCreationTime so that it looks at a single process, without mixing multiple processes with the same process ID. There are numerous ways to construct a command line to accomplish a task. For example, an attacker could reference an image file without a path, without a file extension, using environment variables, or with quotes. The attacker could also change the order of parameters or add multiple quotes and spaces. The following examples show various ways to construct a query that looks for the file net. To incorporate long lists or large tables into your query, use the externaldata operator to ingest data from a specified URI. The example below shows how you can utilize the extensive list of malware SHA hashes provided by MalwareBazaar abuse. There are various functions you can use to efficiently handle strings that need parsing or conversion. To learn about all supported parsing functions, read about Kusto string functions. Some tables in this article might not be available in Microsoft Defender for Endpoint. Do you want to learn more? Skip to main content. This browser is no longer supported. Table of contents Exit focus mode. Table of contents. Note Some tables in this article might not be available in Microsoft Defender for Endpoint. Tip Do you want to learn more? Was this page helpful? Yes No. Provide product feedback. Additional resources In this article. Deconstruct a version number with up to four sections and up to eight characters per section. Use the parsed data to compare version age. Convert an IPv4 address to a long integer.

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