When you first hear about it, SQL Server’s In-Memory OLTP sounds like a new feature that’s hard to implement, requires a lot of schema changes, and takes planning and downtime. Here Brent Ozar tells you how it’s simply not true, and you can do it in just 3 steps.
2019-04-05
In-Memory OLTP is a high performance, memory-optimized engine integrated into SQL Server 2014 and designed for modern hardware trends. In-Memory OLTP allows users to move data into memory resident tables while keeping other data in traditional, disk-based table structures. For performance critical workloads, users can also migrate Transact-SQL code to natively compiled stored procedures. This can provide additional performance gains. This paper aims to help readers understand some of the common architectural patterns where In-Memory OLTP has provided significant benefits. The paper also discusses considerations for migrating applications to In-Memory OLTP.
2018-06-22
2,677 reads
In-Memory OLTP (project “Hekaton”) is a new database engine component, fully integrated into SQL Server. It is optimized for OLTP workloads accessing memory resident data. In-Memory OLTP allows OLTP workloads to achieve significant improvements in performance, and reduction in processing time. Tables can be declared as ‘memory optimized’ to enable In-Memory OLTP’s capabilities. Memory-optimized tables are fully transactional and can be accessed using Transact-SQL. Transact-SQL stored procedures can be compiled to machine code for further performance improvements on memory-optimized tables. The engine is designed for high concurrency and blocking is minimal.
2018-06-21
2,997 reads
In this free ebook Kalen Delaney introduces and explains how the 2016 In-Memory OLTP engine works. The Hekaton internals knowledge offered in this book will help you migrate existing tables or databases to Hekaton, and get faster performance from your SQL Server applications than you ever thought possible.
2018-01-09
5,525 reads
SQL Server's In-memory OLTP is fast, due to its multi-valued concurrency control (MVCC). MVCC avoids the need for locks by arranging for each user connected to the database to see a snapshot of the rows of the tables at a point in time, No changes made by the user will be seen by other users of the database until the changes have been completed and committed. It is conceptually simple but does the user always see the correct version of a row under all circumstances? Shel Burkow explains.
2016-10-17
3,150 reads
In-memory OLTP was introduced in SQL Server 2014, but greatly improved in SQL Server 2016. If you have a heavily-used transaction-processing that is processing a lot of data, it is definitely worth considering the new 'In-Memory' features of SQL Server 2016. Grant Fritchey explains why.
2016-09-19
5,042 reads
There are several decisions to be made when designing indexes for Memory-optimized tables in In-Memory OLTP, and to make the best choice, it pays to understand something about the nature and usage of memory-optimised indexes. Murilo Miranda continues his series by explaining how such an index is unlike a conventional index.
2016-06-10
4,346 reads
How do you go about transferring a disk-based workload to the respective memory-optimized design? How do you process memory-optimized tables? How important for performance are natively-compiled Stored Procedures? Artemakis Artemiou comes up with a step-by-step guide to implementing an in-memory OLTP solution .
2016-05-16
3,732 reads
Aaron Bertrand takes a look at the changes in store for In-Memory OLTP (the feature formerly known as "Hekaton").
2015-12-17
5,473 reads
2015-08-17
4,196 reads
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Comments posted to this topic are about the item Fun with JSON II
Comments posted to this topic are about the item Changing Data Types
I have some data in a table:
CREATE TABLE #test_data
(
id INT PRIMARY KEY,
name VARCHAR(100),
birth_date DATE
);
-- Step 2: Insert rows
INSERT INTO #test_data
VALUES
(1, 'Olivia', '2025-01-05'),
(2, 'Emma', '2025-03-02'),
(3, 'Liam', '2025-11-15'),
(4, 'Noah', '2025-12-22');
If I run this query, how many rows are returned?
SELECT t1.[key] AS row,
t2.*
FROM OPENJSON(
(
SELECT t.* FROM #test_data AS t FOR JSON PATH
)
) t1
CROSS APPLY OPENJSON(t1.value) t2; See possible answers