![]() Additionally, SQL Server needs significantly less space to store, maintain, and back up this compressed data. #JSON QUERY SQL SERVER UPDATE#Using the VARBINARY(MAX) data type with COMPRESS in the INSERT/ UPDATE queries - and DECOMPRESS in the SELECT queries - is a much better design pattern and dramatically reduces the amount of data transferred over the network. Unfortunately, this table participates in thousands of transactions per day, and as the original developers used Entity Framework and didn’t think much of using NVARCHAR(MAX), the entire row is coming over the wire into the application each time it is queried.Īs I’ve written previously about this kind of thing, this is not a good design pattern. #JSON QUERY SQL SERVER ARCHIVE#I’ll also pre-emptively note that if this table was simply an append-only archive table, the row size would not really matter. If you’ve done some mental arithmetic, you’ll also realize that with an average of 10KB per row (and SQL Server having a data page size of 8KB) there’s a lot of off-row nonsense going on here. In other words, almost 100GB of storage is being used by two JSON columns. Not only that, but there are two columns containing between 2KB and 5KB each with over 10 million rows. The remarkable thing - because you read the subject of this post and have figured it out - is that they’re storing JSON data in that table. That table contains just under 10 million rows, which isn’t that remarkable another table in the same database has almost 500 million rows. SELECT po.During routine maintenance on a customer’s production server, I discovered that they have one table consuming 40% of the storage in their database. & exists as a substitution variable and prompt you to define Use SET DEFINE OFF first, so that SQL*Plus does not interpret (If you use this example or similar with SQL*Plus then you must Items the predicate returns true if a match is found, that is, if any such line items The argument to path-expression predicateĮxists is a path expression that specifies particular line Selected by Example 14-4, as well as all documents that have "ABULL" and documents that have a line item with a part that has UPC codeĪnd with an order quantity greater than 3. It selects purchase-order documents that have a User field whose value is One part of a filter while leaving another part scoped at the document (context-item) level. The following SQL data types are supported for such variables: VARCHAR2, NUMBER, BINARY_DOUBLE, DATE, TIMESTAMP, and TIMESTAMP WITH TIMEZONE.Įxample 14-5 JSON_EXISTS: Path Expression Using The optional filter expression of a SQL/JSON path expression used with json_exists can refer to SQL/JSON variables, whose values are passed from SQL by binding them with the PASSING clause. The second argument to json_exists is a SQL/JSON path expression followed by an optional PASSING clause and an optional error clause. Unlike the case for conditions is json and is not json, condition json_exists expects the data it examines to be well-formed JSON data. The handler takes effect when any error occurs, but typically an error occurs when the given JSON data is not well-formed (using lax syntax). You can also use json_exists to create bitmap indexes for use with JSON data - see Example 24-1.Įrror handlers ERROR ON ERROR, FALSE ON ERROR, and TRUE ON ERROR apply. ![]() If no JSON values are matched then it returns false. ![]() ![]() More precisely, json_exists returns true if the data it targets matches one or more JSON values. Condition json_exists checks for the existence of a particular value within JSON data: it returns true if the value is present and false if it is absent. ![]()
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