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d여기에서 What is a Materialized View? – sybase materialized view 주제에 대한 세부정보를 참조하세요
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This video explains the concept of a materialized view in a database also known as materialized query table an indexed view.
Category: Databases
Tags: Materialized View
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My experience with materialized views in Sybase ASE
In this post I’d like to focus on precomputed result sets in SAP Sybase ASE, which are also called materialized views (MVs).
Source: blogs.sap.com
Date Published: 1/4/2022
View: 7648
11113: Support for materialized view in Sybase ASE 15.7+
A new feature request for Sybase ASE 15.7+ materialized views. Take a look at. http://infocenter.sybase.com/help/topic/com.sybase.
Source: www.aquaclusters.com
Date Published: 11/6/2021
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Does materialized view data in sybase 16 get cleared after …
I have Sybase 16 server in my local machine. I have created materialized views for some intermediate tables to improve performance.
Source: stackoverflow.com
Date Published: 8/24/2022
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Limitations and considerations ‒ Qlik Replicate
Materialized views are not supported. Data capture from partitions is only supported from Sybase ASE 15 or later. Merge operations performed during CDC will …
Source: help.qlik.com
Date Published: 1/9/2022
View: 4129
Sybase and materialized views – narkive
For those not familiar with Oracle materialized views … they are tables (not views) that refresh themselves based on a SQL statement
Source: comp.databases.sybase.narkive.com
Date Published: 9/9/2021
View: 1704
How to convert Oracle Materialized View to Sybase – sybase.ase …
Hello, One project I’m working on uses Oracle materialized view. Basically it is a view … Is there a Sybase function I can use to replace it? Thanks Jmgxu …
Source: codeverge.com
Date Published: 8/27/2022
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What are Materialized Views? A 5 Minute Introduction
A materialized view is a type of database object that contains any results … materialized views include PostgreSQL, SQL Server, Sybase SQL …
Source: www.educative.io
Date Published: 12/4/2022
View: 4594
Sybase Vs Oracle | Spiceworks Tech
Further is there a equivalent for Materialized Views in Sybase. Do we have a SQL Command like ‘connect by.. start with..’ in Sybase. Thanx in …
Source: www.spiceworks.com
Date Published: 3/6/2021
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주제에 대한 기사 평가 sybase materialized view
- Author: HandsonERP
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- Date Published: 2013. 5. 3.
- Video Url link: https://www.youtube.com/watch?v=06HlvmB8mDk
What is a materialized view used for?
A materialized view simplifies complex data by saving query information – you don’t have to create a new query every time you need to access the information. The main thing that sets a materialized view apart is that it is a copy of query data that does not run in real-time.
Which is better view or materialized view?
The performance of Materialized view it is better than normal View because the data of materialized view will be stored in table and table may be indexed so faster for joining also joining is done at the time of materialized views refresh time so no need to every time fire join statement as in case of view.
Why use materialized view instead of a table?
Materialized views are basically used to increase query performance since it contains results of a query. They should be used for reporting instead of a table for a faster execution.
What is the benefit of materialized view?
Benefits of using materialized views
Reduce the execution time for complex queries with JOINs and aggregate functions. The more complex the query, the higher the potential for execution-time saving. The most benefit is gained when a query’s computation cost is high and the resulting data set is small.
What is the limitations of materialized view?
Materialized views cannot be nested on other materialized views. Materialized views cannot query external tables. Only the standard SQL dialect is supported for materialized views. If you delete a base table without first deleting the materialized view, queries over the materialized view fail, as do refreshes.
How often is materialized view refresh?
Unlike indexes, materialized views are not automatically updated with every data change. They must explicitly be refreshed, either on every commit, on a periodically time schedule or – typically in data warehouses – at the end of an ETL job.
Does materialized view occupy space?
A materialized view occupies space. It exists in the same way as a table: it sits on a disk and could be indexed or partitioned.
Can we insert data into materialized view?
You can’t insert data into a materialized view as you can with a table. To update the contents of a materialized view, you can execute a query to refresh it. This will re-execute the query used to create it.
Can we update materialized view?
The data in the materialized view remains unchanged, even when applications make changes to the data in the underlying tables. To update the data in a materialized view, you can use the REFRESH MATERIALIZED VIEW statement at any time.
What is a main difference between regular views and materialized views?
Views are generally used when data is to be accessed infrequently and data in table get updated on frequent basis. On other hand Materialized Views are used when data is to be accessed frequently and data in table not get updated on frequent basis.
Can we create materialized view on multiple tables?
– You cannot create a fast refreshable materialized view from multiple tables with simple joins that include an object type column in the SELECT statement.
Can we perform DML on materialized view?
A materialized view can be either read-only, updatable, or writeable. Users cannot perform data manipulation language (DML) statements on read-only materialized views, but they can perform DML on updatable and writeable materialized views.
Is materialized view just a table?
Materialized views are physically exist in database. Whenever the base table is updated the Materialized view gets updated. Materialized views are updated periodically based upon the query definition, table can not do this.
Is materialized view faster than view?
Materialized View responds faster than View as the Materialized View is precomputed. Materialized View utilizes the memory space as it stored on the disk whereas, the View is just a display hence it do not require memory space.
Does materialized view occupy space?
A materialized view occupies space. It exists in the same way as a table: it sits on a disk and could be indexed or partitioned.
My experience with materialized views in Sybase ASE
In this post I’d like to focus on precomputed result sets in SAP Sybase ASE, which are also called materialized views (MVs). Basically, an MV is a calculated result set of a query based on one or more tables. Unlike regular views, the result of the materialized view is stored in a database much like a regular table. MVs can be used to (semi)automatic pre-calculation of result sets of relatively costly queries, thus allowing applications to perform much simpler and faster queries from MVs than going to regular tables again and again. Usually, MVs are used for pre-joining and pre-aggregation of the data for data warehouse applications, but it can be useful in OLTP as well, as will be shown later.
The concept of MV is anything but new in the database world; Oracle has implemented it first in version 8.1.5, back in 2000. In ASE, precomputed result sets first appeared in ASE 15.7 ESD#2. For some obscure reason, SAP/Sybase doesn’t advertise this very useful and long awaited feature too much. To my surprise, this feature was not mentioned at all in the last SAP Teched 2013 conference in Las Vegas, at least not in presentations known to me. You are welcome to read the documentation here . I’d like very much to see MVs also in SAP Sybase IQ, but it is a completely different story.
MVs can be queried by applications just like regular tables, therefore we may want to create appropriate indexes on MVs and also update statistics on relevant columns. While it is certainly possible for applications to query MVs directly, the most powerful use of MVs is via automatic query rewriting. “Query rewrite” means that the optimizer is able to automatically substitute parts of queries from base tables with queries from suitable MVs, when applicable. It is a great way to dramatically improve performance of existing applications without touching the application code. When the optimizer “feels like” rewriting of a query (or some parts of the query) using relevant MVs, which possibly contains pre-joined or pre-aggregated data, it will just do that. The result set of the query, of course, remains the same with and without MVs and it is optimizer’s responsibility to care about it.
A change in the data of a table that the MV is based on may cause the data of the MV to become stale (not up-to-date). The MV can be refreshed automatically, as a part of the transaction that updates the base table, or manually. If an MV is refreshed automatically, the data in the MV doesn’t in fact become stale, it always stays in-tact with the base table data, or in other words, fresh. In the case of the manual refresh of an MV, using an explicit REFRESH command, the data in the MV becomes stale immediately after the refresh. It is up to the application to decide using “set materialized_view_optimization” command if stale MVs are acceptable for query rewriting. Sometimes, the data returned by a query doesn’t have to be 100% up-to-date and lagging by a number of minutes/hours, for example, may be bearable.
Now, the background is over and we are ready to move to the actual use case. One of my customers has an application that presents a list of events of some kind, with pagination. In addition, the total number of events of different kinds must be displayed on the screen. New events flow to the system all the time and there is a requirement to present the exact number of events each time. The query executed by the customer’s application to get the count of relevant events looks like this:
select isnull(count(*), 0) from events_big l where ISNULL(l.event_type1, ‘?’) NOT IN (‘A’,’G’,’P’,’L’) and l.event_is_display = ‘Y’
This query is certainly not ideal, but it is next to impossible to change it in a foreseeable future. It performs full table scan of quite a fat table (many tens of columns), because some query arguments are not very SARGable and also because about 80-90% of events answer the conditions of the query. The query performance is OK with tens of thousands of rows in the table. However, becomes considerably slow when the number of rows in the table reaches millions (1.5 million in my test case). Even when the whole table is in cache, the query returns in 1-2 seconds, which is a lot for a frequently executed OLTP query. Reads from the table clearly outnumber writes, so I decided to try to rewrite this query using a materialized view without changing the application. I was quite optimistic about this, but unfortunately I have hit a number of unexpected limitations of MVs in ASE:
1. It appears that MVs with “count(*)” are not allowed, you just cannot create an MV with “count(*)” inside. Instead, MVs should use count_big(*). I must admit that I was unaware of the very existence of count_big(*) before I hit this problem. This limitation virtually eliminates the possibility to utilize MVs for existing applications, that don’t use count_big(*), without changes in the application, which usually implies QA etc., which is in turn not cheap at all and not always possible.
2. Using of ISNULL function in an MV is not allowed.
3. Setting of this set of options is mandatory to create and use MVs:
set ansinull on set arithabort on set arithignore off set string_rtruncation on
These settings are anything but default ones. While it may be implemented quite easily in a login trigger, possible functionality changes in a big legacy application will require a thorough QA.
4. Just for the test purpose, let’s assume that we can work around all limitations mentioned above and change the application. If so, we can use an MV like the shown below to rewrite the query and in this case the automatic query rewrite indeed works:
— the changed query, which is logically the same as the original one — and gets rewritten using an MV automatically: select count_big(*) fcount from events_big l where l.event_type1 NOT IN (‘A’,’G’,’P’,’L’) and l.event_is_display = ‘Y’ and l.event_type1 IS NOT NULL — The materialized view definition create precomputed result set prs_events_big_tailored immediate refresh as select count_big(*) fcount from events_big l where l.event_type1 NOT IN (‘A’,’G’,’P’,’L’) and l.event_is_display = ‘Y’ and l.event_type1 IS NOT NULL
Using the automatic query rewrite, the number of logical reads in my test case drop from 502317 to 1 and the response time from 1-2 second to milliseconds – which is quite impressive. However, what happens if we want to be more generic and we want to hold a single MV to serve a number of similar queries (an actual requirement in my customer’s case)? I tried to use the following, more generic materialized view:
create precomputed result set prs_events_big_generic unique(l.event_type1, l.event_is_display) immediate refresh as select l.event_type1, l.event_is_display, count_big(*) fcount from events_big l group by l.event_type1, l.event_is_display
Unfortunately, the query from the previous example doesn’t get rewritten and it is a serious limitation. It is not feasible to maintain an MV for each query that should be rewritten. Another important limitation I have found is that it is very difficult to know why the query rewrite didn’t occur, in fact we have nothing but guess here.
Just to be sure that I’m not asking ASE to do something obviously impossible, I performed the same test in Oracle 11.2.0.1 with exactly the same data. It appeared that none of the MV limitations explained above exist in Oracle. Oracle was able to use prs_events_big_generic MV to rewrite the original query; I mean the original query without my fixes. In addition, Oracle supplies DBMS_MVIEW.EXPLAIN_REWRITE procedure, which allows to see if the query rewrite of a specified query using the specified MV is possible, and if it isn’t – shows the reason why. I have found DBMS_MVIEW.EXPLAIN_REWRITE procedure very useful.
My conclusions: SAP has finally implemented precomputed result sets or materialized views in ASE and it is very good. Unfortunately, this new great feature still suffers from some significant limitations, which are not always properly documented. If you have experience with MVs in databases from other vendors and plan to implement MVs in ASE – perform thorough functionality tests first, you may hit additional limitations that I didn’t find during my research.
P.S. All my tests with materialized views are fully reproducible. I can send my data set to anyone who wishes to repeat my tests.
This post has been originally published at http://leonid-gvirtz.typepad.com/blog/2013/11/my-experience-with-materialized-views-in-sybase-ase.html
Materialized View: What is It and Why Should You Use It?
Written by Nahla Davies• 10:28• Database development, Views • One Comment
As data volumes continue to increase, more developers look towards materialized views to process queries. This approach has a lot of potentials.
We are discovering that apps and software are the next steps in creating a more connected future. Data solutions such as cloud computing, wireless communication, and the internet of things (IoT) are all crucial for the advancement of technologies.
Cloud computing and pre-computational processes, such as materialized views, used together bring the companies more control of their data solutions. Using a materialized view to execute queries instead of traditional ways of viewing data tables is a significant step towards a deeper understanding of storing and extracting data.
This article will focus on the idea of a materialized view, its conditions, and the benefits it brings to users. You’ll also learn the difference between a view and a materialized view, and why you should take advantage of a materialized view if you haven’t already.
What is a Materialized View?
A materialized view simplifies complex data by saving query information – you don’t have to create a new query every time you need to access the information.
The main thing that sets a materialized view apart is that it is a copy of query data that does not run in real-time. It takes a little more space, but it also retrieves data very quickly. You can set materialized views to get refreshed on a schedule so that the updated information won’t fall through the cracks.
When used in conjunction with other development tools, a materialized view can create a more secure environment for your database.
According to Barbara Ericson of Cloud Defense, “a development team could introduce a SAST tool in their development workflow and only utilize the security checks for catching specific errors, like SQL injection issues. As the developers learn about the tool and see how it catches SQL injection errors, they’ll adapt more quickly and see the value that can provide to the development pipeline. This is more effective in a practical sense as well as a better strategy for encouraging developers to adopt DevSecOps practices wholesale.”
Oracle was the first database to make materialized views available. Since 8i, it has been a part of every release. Not all environments support materialized views, as it is a fairly modern database tool. However, SQL Server, Sybase SQL Anywhere, and now PostgreSQL and BigQuery all support them.
The importance of using materialized views is more than just increasing productivity. Moore’s law suggests that computers and computing power will become smaller and less expensive as time goes on. There is an impending limitation that we are quickly approaching, though. Technological advances in computing and data storage are going beyond physical capabilities.
Materialized View vs View
Both a view and a materialized view can be very useful for simplifying and optimizing data. You can join data from multiple tables and compile the information into one simple table.
To better understand what benefits a materialized view brings, let’s compare it to a regular view.
A view is a precise virtual table that collects data you have previously gathered from any other relevant query. Anytime you access the view, it recompiles the data to provide you with the most up-to-date information according to your query.
You can make changes in the view or the base tables, and the data will be updated automatically in both places. Thus, if you make a change in a view, it will push that data to the base table. A change to the base table is automatically pushed to the view. Because of these steps, it may take some time for the regular view to get updated.
A regular view is great because it doesn’t take much space. But it sacrifices speed and performance.
A materialized view is much more efficient at executing queries. The data is physically saved at a specific point in time. You don’t need to re-read all the data associated with a query every single time.
The drawback is that you have to make sure to view the most recent data. To reduce the risk of viewing obsolete data, you can refresh it manually, or set it to refresh on schedule or by triggers.
Should you Use a Materialized View?
When you consider what a materialized view does, you may wonder why few seconds are worth extra space to save a copy of the data.
First of all, it is about cost-efficiency. Every time you query a database, there are costs involved. Applications evolve to become more resource-intensive. Thus, each step of query execution (parsing, validating, planning, optimizing) equates to CPU time, memory usage, and cost accrual.
Materialized views are essential in cutting costs for developers. The results obtained in a materialized view are kept in memory. They are only updated when needed, not constantly. So, you improve the performance by precomputing some of the most expensive operations. Additionally, the speed can increase greatly when it comes to querying large databases.
Unfortunately, the use of materialized views may not suit every situation. First, not every database supports materialized views (Jump to What is a materialized view for information on environments that do support them).
There are other issues too. Material views are read-only. This means that you can’t update tables from a material view like you can with a regular view. Also, even though material views are pretty secure, there are still security risks since some security features are missing. For example, you can’t create security keys or constraints with a material view.
Materialized View: Tips for Using
You should keep in mind some features to ensure getting the most from a materialized view:
Make sure that you are working with the materialized view that reflects query patterns against the base table. You don’t want to create a materialized view for every single iteration of a query. That would defeat the purpose. Create a materialized view that will focus on a broad set of queries.
If your original table is partitioned, you should partition the materialized view as well. If you don’t, it may become larger than you want. Partitioning the materialized view when to match the partitioned base table ensures that you preserve the performance.
Be mindful of open-source platforms. You need to consider how they affect your vulnerability as you create materialized views. Using own code is free from vulnerabilities that affect APIs. It can improve your performance, and it is harder to hack.
Conclusion
Using materialized views instead of extracting data from tables on their own or using a regular view can help optimize performance and increase data productivity. Check if your database supports materialized views, and make the most of the database connectivity.
Many modern tools for coding and developing help the specialists. In addition to taking advantage of these tools, use materialized views to decrease your data costs and increase the speed of queries. Even though the cost of computers has decreased over time, the cost of data remains much higher. Finding ways to decrease data and storage costs, such as using materialized views, is going to be key, as we advance even further into the digital age.
Last modified: September 23, 2021
What is the difference between Views and Materialized Views in Oracle?
Views
They evaluate the data in the tables underlying the view definition at the time the view is queried. It is a logical view of your tables, with no data stored anywhere else.
The upside of a view is that it will always return the latest data to you. The downside of a view is that its performance depends on how good a select statement the view is based on. If the select statement used by the view joins many tables, or uses joins based on non-indexed columns, the view could perform poorly.
Materialized views
They are similar to regular views, in that they are a logical view of your data (based on a select statement), however, the underlying query result set has been saved to a table. The upside of this is that when you query a materialized view, you are querying a table, which may also be indexed.
In addition, because all the joins have been resolved at materialized view refresh time, you pay the price of the join once (or as often as you refresh your materialized view), rather than each time you select from the materialized view. In addition, with query rewrite enabled, Oracle can optimize a query that selects from the source of your materialized view in such a way that it instead reads from your materialized view. In situations where you create materialized views as forms of aggregate tables, or as copies of frequently executed queries, this can greatly speed up the response time of your end user application. The downside though is that the data you get back from the materialized view is only as up to date as the last time the materialized view has been refreshed.
Materialized views can be set to refresh manually, on a set schedule, or based on the database detecting a change in data from one of the underlying tables. Materialized views can be incrementally updated by combining them with materialized view logs, which act as change data capture sources on the underlying tables.
Materialized views are most often used in data warehousing / business intelligence applications where querying large fact tables with thousands of millions of rows would result in query response times that resulted in an unusable application.
Materialized views also help to guarantee a consistent moment in time, similar to snapshot isolation.
Why use materialized view instead of a table?
What is Materialized views? Explain its uses. In general, a view represents a queries result set. When this query result is cached and updated from the base table’s periodically, it is called as a materialized view. It is most commonly used in scenarios where frequent query data needs to be accessed. Unlike a view, materialized view is not virtual. It is typically used in data warehousing. Using materialized view network load is reduced. Why use materialized view instead of a table? Materialized views are basically used to increase query performance since it contains results of a query. They should be used for reporting instead of a table for a faster execution.
Performance tune with materialized views – Azure Synapse Analytics
Table of contents
Performance tune with materialized views
Article
05/25/2022
10 minutes to read
8 contributors
In this article
Materialized views for dedicated SQL pools in Azure Synapse provide a low maintenance method for complex analytical queries to get fast performance without any query change. This article discusses the general guidance on using materialized views.
Materialized views vs. standard views
Dedicated SQL pool in Azure Synapse supports standard and materialized views. Both are virtual tables created with SELECT expressions and presented to queries as logical tables. Views encapsulate the complexity of common data computation and add an abstraction layer to computation changes so there’s no need to rewrite queries.
A standard view computes its data each time when the view is used. There’s no data stored on disk. People typically use standard views as a tool that helps organize the logical objects and queries in a dedicated SQL pool. To use a standard view, a query needs to make direct reference to it.
A materialized view pre-computes, stores, and maintains its data in a dedicated SQL pool just like a table. There’s no recomputation needed each time a materialized view is used. That’s why queries that use all or a subset of the data in materialized views can get faster performance. Even better, queries can use a materialized view without making direct reference to it, so there’s no need to change application code.
Most of the requirements on a standard view still apply to a materialized view. For details on the materialized view syntax and other requirements, refer to CREATE MATERIALIZED VIEW AS SELECT
Comparison View Materialized View View definition Stored in dedicated SQL pool. Stored in dedicated SQL pool. View content Generated each time when the view is used. Pre-processed and stored in dedicated SQL pool during view creation. Updated as data is added to the underlying tables. Data refresh Always updated Always updated Speed to retrieve view data from complex queries Slow Fast Extra storage No Yes Syntax CREATE VIEW CREATE MATERIALIZED VIEW AS SELECT
Benefits of using materialized views
A properly designed materialized view provides the following benefits:
Reduce the execution time for complex queries with JOINs and aggregate functions. The more complex the query, the higher the potential for execution-time saving. The most benefit is gained when a query’s computation cost is high and the resulting data set is small.
The optimizer in dedicated SQL pool can automatically use deployed materialized views to improve query execution plans. This process is transparent to users providing faster query performance and doesn’t require queries to make direct reference to the materialized views.
Require low maintenance on the views. All incremental data changes from the base tables are automatically added to the materialized views in a synchronous manner, meaning both the base tables and the materialized views are updated in the same transaction. This design allows querying materialized views to return the same data as directly querying the base tables.
The data in a materialized view can be distributed differently from the base tables.
Data in materialized views gets the same high availability and resiliency benefits as data in regular tables.
The materialized views implemented in dedicated SQL pool also provide the following benefits:
Compared to other data warehouse providers, the materialized views implemented in dedicated SQL pool also provide the following benefits:
Broad aggregate function support. See CREATE MATERIALIZED VIEW AS SELECT (Transact-SQL).
The support for query-specific materialized view recommendation. See EXPLAIN (Transact-SQL).
Automatic and synchronous data refresh with data changes in base tables. No user action is required.
Common scenarios
Materialized views are typically used in the following scenarios:
Need to improve the performance of complex analytical queries against large data in size
Complex analytical queries typically use more aggregate functions and table joins, causing more compute-heavy operations such as shuffles and joins in query execution. That’s why complex analytical queries take longer to complete, especially on large tables.
Users can create materialized views for the data returned from common computations of queries, so there’s no recomputation needed when this data is needed by queries, allowing lower compute cost and faster query response.
Need faster performance with no or minimum query changes
Schema and query changes in dedicated SQL pools are typically kept to a minimum to support regular ETL operations and reporting. People can use materialized views for query performance tuning, if the cost incurred by the views can be offset by the gain in query performance.
In comparison to other tuning options such as scaling and statistics management, it’s a less impactful production change to create and maintain a materialized view and its potential performance gain is also higher.
Creating or maintaining materialized views does not impact the queries running against the base tables.
The query optimizer can automatically use the deployed materialized views without direct view reference in a query. This capability reduces the need for query change in performance tuning.
Need different data distribution strategy for faster query performance
Dedicated SQL pool is a distributed query processing system. Data in a SQL table is distributed upto 60 nodes using one of three distribution strategies (hash, round_robin, or replicated).
The data distribution is specified at the table creation time and stays unchanged until the table is dropped. Materialized view, being a virtual table on disk, supports hash and round_robin data distributions. Users can choose a data distribution that is different from the base tables but optimal for the performance of queries that use the views.
Design guidance
Here is the general guidance on using materialized views to improve query performance:
Design for your workload
Before you begin to create materialized views, it’s important to have a deep understanding of your workload in terms of query patterns, importance, frequency, and the size of resulting data.
Users can run EXPLAIN WITH_RECOMMENDATIONS
for the materialized views recommended by the query optimizer. Since these recommendations are query-specific, a materialized view that benefits a single query may not be optimal for other queries in the same workload. Evaluate these recommendations with your workload needs in mind. The ideal materialized views are those that benefit the workload’s performance.
Be aware of the tradeoff between faster queries and the cost
For each materialized view, there’s a data storage cost and a cost for maintaining the view. As data changes in base tables, the size of the materialized view increases and its physical structure also changes. To avoid query performance degradation, each materialized view is maintained separately by the SQL engine.
The maintenance workload gets higher when the number of materialized views and base table changes increase. Users should check if the cost incurred from all materialized views can be offset by the query performance gain.
You can run this query to generate a list of materialized views in a dedicated SQL pool:
SELECT V.name as materialized_view, V.object_id FROM sys.views V JOIN sys.indexes I ON V.object_id= I.object_id AND I.index_id < 2; Options to reduce the number of materialized views: Identify common data sets frequently used by the complex queries in your workload. Create materialized views to store those data sets so the optimizer can use them as building blocks when creating execution plans. Drop the materialized views that have low usage or are no longer needed. A disabled materialized view is not maintained but it still incurs storage cost. Combine materialized views created on the same or similar base tables even if their data doesn't overlap. Combining materialized views could result in a larger view in size than the sum of the separate views, however the view maintenance cost should reduce. For example: -- Query 1 would benefit from having a materialized view created with this SELECT statement SELECT A, SUM(B) FROM T GROUP BY A -- Query 2 would benefit from having a materialized view created with this SELECT statement SELECT C, SUM(D) FROM T GROUP BY C -- You could create a single mateiralized view of this form SELECT A, C, SUM(B), SUM(D) FROM T GROUP BY A, C Not all performance tuning requires query change The SQL query optimizer can automatically use deployed materialized views to improve query performance. This support is applied transparently to queries that don't reference the views and queries that use aggregates unsupported in materialized views creation. No query change is needed. You can check a query's estimated execution plan to confirm if a materialized view is used. Monitor materialized views A materialized view is stored in the dedicated SQL pool just like a table with a clustered columnstore index (CCI). Reading data from a materialized view includes scanning the CCI index segments and applying any incremental changes from base tables. When the number of incremental changes is too high, resolving a query from a materialized view can take longer than directly querying the base tables. To avoid query performance degradation, it's a good practice to run DBCC PDW_SHOWMATERIALIZEDVIEWOVERHEAD to monitor the view's overhead_ratio (total_rows / max(1, base_view_row)). Users should REBUILD the materialized view if its overhead_ratio is too high. Materialized view and result set caching These two features in dedicated SQL pool are used for query performance tuning. Result set caching is used for getting high concurrency and fast response from repetitive queries against static data. To use the cached result, the form of the cache requesting query must match with the query that produced the cache. In addition, the cached result must apply to the entire query. Materialized views allow data changes in the base tables. Data in materialized views can be applied to a piece of a query. This support allows the same materialized views to be used by different queries that share some computation for faster performance. Example This example uses a TPCDS-like query that finds customers who spend more money via catalog than in stores, identify the preferred customers and their country/region of origin. The query involves selecting TOP 100 records from the UNION of three sub-SELECT statements involving SUM() and GROUP BY. WITH year_total AS ( SELECT c_customer_id customer_id ,c_first_name customer_first_name ,c_last_name customer_last_name ,c_preferred_cust_flag customer_preferred_cust_flag ,c_birth_country customer_birth_country ,c_login customer_login ,c_email_address customer_email_address ,d_year dyear ,sum(isnull(ss_ext_list_price-ss_ext_wholesale_cost-ss_ext_discount_amt+ss_ext_sales_price, 0)/2) year_total ,'s' sale_type FROM customer ,store_sales ,date_dim WHERE c_customer_sk = ss_customer_sk AND ss_sold_date_sk = d_date_sk GROUP BY c_customer_id ,c_first_name ,c_last_name ,c_preferred_cust_flag ,c_birth_country ,c_login ,c_email_address ,d_year UNION ALL SELECT c_customer_id customer_id ,c_first_name customer_first_name ,c_last_name customer_last_name ,c_preferred_cust_flag customer_preferred_cust_flag ,c_birth_country customer_birth_country ,c_login customer_login ,c_email_address customer_email_address ,d_year dyear ,sum(isnull(cs_ext_list_price-cs_ext_wholesale_cost-cs_ext_discount_amt+cs_ext_sales_price, 0)/2) year_total ,'c' sale_type FROM customer ,catalog_sales ,date_dim WHERE c_customer_sk = cs_bill_customer_sk AND cs_sold_date_sk = d_date_sk GROUP BY c_customer_id ,c_first_name ,c_last_name ,c_preferred_cust_flag ,c_birth_country ,c_login ,c_email_address ,d_year UNION ALL SELECT c_customer_id customer_id ,c_first_name customer_first_name ,c_last_name customer_last_name ,c_preferred_cust_flag customer_preferred_cust_flag ,c_birth_country customer_birth_country ,c_login customer_login ,c_email_address customer_email_address ,d_year dyear ,sum(isnull(ws_ext_list_price-ws_ext_wholesale_cost-ws_ext_discount_amt+ws_ext_sales_price, 0)/2) year_total ,'w' sale_type FROM customer ,web_sales ,date_dim WHERE c_customer_sk = ws_bill_customer_sk AND ws_sold_date_sk = d_date_sk GROUP BY c_customer_id ,c_first_name ,c_last_name ,c_preferred_cust_flag ,c_birth_country ,c_login ,c_email_address ,d_year ) SELECT TOP 100 t_s_secyear.customer_id ,t_s_secyear.customer_first_name ,t_s_secyear.customer_last_name ,t_s_secyear.customer_birth_country FROM year_total t_s_firstyear ,year_total t_s_secyear ,year_total t_c_firstyear ,year_total t_c_secyear ,year_total t_w_firstyear ,year_total t_w_secyear WHERE t_s_secyear.customer_id = t_s_firstyear.customer_id AND t_s_firstyear.customer_id = t_c_secyear.customer_id AND t_s_firstyear.customer_id = t_c_firstyear.customer_id AND t_s_firstyear.customer_id = t_w_firstyear.customer_id AND t_s_firstyear.customer_id = t_w_secyear.customer_id AND t_s_firstyear.sale_type = 's' AND t_c_firstyear.sale_type = 'c' AND t_w_firstyear.sale_type = 'w' AND t_s_secyear.sale_type = 's' AND t_c_secyear.sale_type = 'c' AND t_w_secyear.sale_type = 'w' AND t_s_firstyear.dyear+0 = 1999 AND t_s_secyear.dyear+0 = 1999+1 AND t_c_firstyear.dyear+0 = 1999 AND t_c_secyear.dyear+0 = 1999+1 AND t_w_firstyear.dyear+0 = 1999 AND t_w_secyear.dyear+0 = 1999+1 AND t_s_firstyear.year_total > 0 AND t_c_firstyear.year_total > 0 AND t_w_firstyear.year_total > 0 AND CASE WHEN t_c_firstyear.year_total > 0 THEN t_c_secyear.year_total / t_c_firstyear.year_total ELSE NULL END > CASE WHEN t_s_firstyear.year_total > 0 THEN t_s_secyear.year_total / t_s_firstyear.year_total ELSE NULL END AND CASE WHEN t_c_firstyear.year_total > 0 THEN t_c_secyear.year_total / t_c_firstyear.year_total ELSE NULL END > CASE WHEN t_w_firstyear.year_total > 0 THEN t_w_secyear.year_total / t_w_firstyear.year_total ELSE NULL END ORDER BY t_s_secyear.customer_id ,t_s_secyear.customer_first_name ,t_s_secyear.customer_last_name ,t_s_secyear.customer_birth_country OPTION ( LABEL = ‘Query04-af359846-253-3’);
Check the query’s estimated execution plan. There are 18 shuffles and 17 joins operations, which take more time to execute. Now let’s create one materialized view for each of the three sub-SELECT statements.
CREATE materialized view nbViewSS WITH (DISTRIBUTION=HASH(customer_id)) AS SELECT c_customer_id customer_id ,c_first_name customer_first_name ,c_last_name customer_last_name ,c_preferred_cust_flag customer_preferred_cust_flag ,c_birth_country customer_birth_country ,c_login customer_login ,c_email_address customer_email_address ,d_year dyear ,sum(isnull(ss_ext_list_price-ss_ext_wholesale_cost-ss_ext_discount_amt+ss_ext_sales_price, 0)/2) year_total , count_big(*) AS cb FROM dbo.customer ,dbo.store_sales ,dbo.date_dim WHERE c_customer_sk = ss_customer_sk AND ss_sold_date_sk = d_date_sk GROUP BY c_customer_id ,c_first_name ,c_last_name ,c_preferred_cust_flag ,c_birth_country ,c_login ,c_email_address ,d_year GO CREATE materialized view nbViewCS WITH (DISTRIBUTION=HASH(customer_id)) AS SELECT c_customer_id customer_id ,c_first_name customer_first_name ,c_last_name customer_last_name ,c_preferred_cust_flag customer_preferred_cust_flag ,c_birth_country customer_birth_country ,c_login customer_login ,c_email_address customer_email_address ,d_year dyear ,sum(isnull(cs_ext_list_price-cs_ext_wholesale_cost-cs_ext_discount_amt+cs_ext_sales_price, 0)/2) year_total , count_big(*) as cb FROM dbo.customer ,dbo.catalog_sales ,dbo.date_dim WHERE c_customer_sk = cs_bill_customer_sk AND cs_sold_date_sk = d_date_sk GROUP BY c_customer_id ,c_first_name ,c_last_name ,c_preferred_cust_flag ,c_birth_country ,c_login ,c_email_address ,d_year GO CREATE materialized view nbViewWS WITH (DISTRIBUTION=HASH(customer_id)) AS SELECT c_customer_id customer_id ,c_first_name customer_first_name ,c_last_name customer_last_name ,c_preferred_cust_flag customer_preferred_cust_flag ,c_birth_country customer_birth_country ,c_login customer_login ,c_email_address customer_email_address ,d_year dyear ,sum(isnull(ws_ext_list_price-ws_ext_wholesale_cost-ws_ext_discount_amt+ws_ext_sales_price, 0)/2) year_total , count_big(*) AS cb FROM dbo.customer ,dbo.web_sales ,dbo.date_dim WHERE c_customer_sk = ws_bill_customer_sk AND ws_sold_date_sk = d_date_sk GROUP BY c_customer_id ,c_first_name ,c_last_name ,c_preferred_cust_flag ,c_birth_country ,c_login ,c_email_address ,d_year
Check the execution plan of the original query again. Now the number of joins changes from 17 to 5 and there’s no shuffle. Select the Filter operation icon in the plan, its Output List shows the data is read from the materialized views instead of the base tables.
With materialized views, the same query runs faster without a code change.
Next steps
For more development tips, see Dedicated SQL pool development overview.
#11113: Support for materialized view in Sybase ASE 15.7+
Support for materialized view in Sybase ASE 15.7+
A new feature request for Sybase ASE 15.7+ materialized views.
Take a look at
http://infocenter.sybase.com/help/topic/com.sybase.infocenter.dc01165.1572/doc/html/mas1329956911672.html
example:
create table mytable (
a int
, b varchar(20)
, constraint mytable_pk primary key nonclustered (a)
)
go
create table another (
a int
, c varchar(10)
, constraint another_pk primary key nonclustered (a)
)
go
insert mytable select 1, ‘abc’
insert mytable select 2, ‘def’
insert another select 1, ‘ghi’
insert another select 2, ‘jkl’
go
— To prevent:
— Precomputed result set cannot be created as some options have an inappropriate setting. Set ansinull on, arithabort on, arithignore off and string_rtruncation on.
set ansinull, arithabort, string_rtruncation on
set arithignore off
go
drop materialized view myview
go
create materialized view myview
constraint myview_ind1 unique (a)
as
select mt.a, mt.b, ant.c
from mytable mt
, another ant
where ant.a = mt.a
go
select *
from myview
go
insert mytable select 3, ‘zzz’
go
select *
from myview
go
insert another select 3, ‘yyy’
go
select *
from myview
go
Does materialized view data in sybase 16 get cleared after database server reboot
I have Sybase 16 server in my local machine. I have created materialized views for some intermediate tables to improve performance. I have the setting for the view as ‘IMMEDIATE REFRESH’ so that the view is updated when the underlying tables are updated.
Since materialized views physically store the data, if I reboot my local machine will the data get cleared? If yes, then when will the data for the view get reloaded?
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Sybase and materialized views
Post by DA Morgan
Hi I’m new to Sybase from Oracle. Does Sybase have anything
equivalent to materialzed views? If not want is the best practice
for creating views with huge amounts of data? Other than indexing
the tables. Thanks for help.
For those not familiar with Oracle materialized views … they are
tables (not views) that refresh themselves based on a SQL statement
based on criteria supplied during their creation. Multiple
materialized views can be made to refresh as part of a single
transaction so as to guarantee their data consistency.
I too am looking for the answer.
—
Daniel A. Morgan
http://www.psoug.org
(replace x with u to respond)
For those not familiar with Oracle materialized views … they aretables (not views) that refresh themselves based on a SQL statementbased on criteria supplied during their creation. Multiplematerialized views can be made to refresh as part of a singletransaction so as to guarantee their data consistency.I too am looking for the answer.–Daniel A. Morganhttp://www.psoug.org(replace x with u to respond)
15.0 does have materialized [deterministic | computed] columns, but
this seems different from your description.)
(2) I imagine that the equivalent functionality could be done with
triggers on the base table(s) that would update the
aggregate/dimension/whatever “materialized view”.
(1) It doesn’t seem to exist in Sybase, at least with that name. (ASE15.0 does have materialized [deterministic | computed] columns, butthis seems different from your description.)(2) I imagine that the equivalent functionality could be done withtriggers on the base table(s) that would update theaggregate/dimension/whatever “materialized view”.
Apparently Oracle materialized views are more efficient than this
approach?
(3) I would think that this capability is not best suited for OLTP
(too frequent activity) environments.
And static tables can be created/updated in batch DSS environments.
(4) Why does Oracle designate a table as a materialized “view”?
—
Galen Boyer
Its a reasonable way to look at it, for understanding them.Oracle uses its own logging mechanism on the tables being queried toknow how to update the table.It can be tuned quite a bit by a DBA that knows what she is doing.There are two uses for these very nifty features. One is really areplacment for homegrown batch processes which build reportingtables/marts/dws. Instead of putting the insert/update/refresh logic inplace with code, UNIX scripts, cron, … jobs, just code the SQL thatgets the data needed, slap “create materialized view” on top of it andthen set the parameters of the materialized view to let Oracle “batch”it up. You can then, either query the view directly, or use them toinsert to the final tables.But, the much more interesting usage (even though the previous one isquite nice), IMHO, is that these things can become very sophisticatedindexes for an already working application. It involves setting theinstance parameter, query_rewrite to true (I think thats the parm andsetting). Without changing anything about an application, one can speedup retrieval by adding an index, correct? Okay, suppose your query thatis taking a long time is something like:SELECT code, count(*)FROM some_tableGROUP BY code;This is sitting somewhere in your application and cannot be touched, andis taking too long. Well,CREATE materialized view code_grouper_byerASSELECT code, count(*)FROM some_tableGROUP BY code;Then, with query_rewrite enabled, the next time the code in the appruns, Oracle will know by its meta data that it already has the answerstored by the materialized view and “rewrite” the query to access thematerialized view. As data changes happen to the underlying”some_table”, Oracle will keep code_grouper_byer up-do-date as well.The complexity of the view’s code can be pretty hairy, and Oracleusually can handle it. I think the limitation it has is when you useanalytics within the view.
How to convert Oracle Materialized View to Sybase
Anyone if the use of Oracle “Materialized Views” is supported by the IDM 3 JDBC driver – docs mention tables and views – no mention of materialized views – I guess you’d just treat them as tables ? > Anyone if the use of Oracle “Materialized Views” is supported by the IDM 3 > JDBC driver – docs mention tables and views – no mention of materialized > views – I guess you’d just treat them as tables ? Yes! When you create an MV in Oracle you should notice that it also creates a corresponding table with the same name in the same schema. If you jus…Hello, I would like to do reverse engineering database Oracle 10R2 using PD 15.1. The DB has a lot of materialized views which are important for DB function, but PD is not able to revers a materialized views ? Please any ideas? Thanks Pepe How are you attempting to reverse, eg. native, jdbc, ODBC ? Do you not, in your select preview, see Materialized view tab (scroll tabs right) or does the tab come up empty ? The user that you use for connecting & revere engineering must have visibility on Mat Views. You can also reverse from the DDL script (.sql) that des…Does any one know if PD 10 can generate Oracle dimension object definitions from PD 10’s Multidimensional Diagram? Also, can it generate Oracle’s Materialized View deinition from the Multidimensional Diagram directly? If not, PD is simply nothing more than a dressed-up Visio. Materialized views are certainly supported. I’m not sure about the Oracle dimension objects, having never used it before. Is this a new thing Oracle added in 10g? Please bring this to Sybase tech support and they can either figure it out or get the functionality added. I love your statement that …Does any one know if PD 10 can generate Oracle dimension object definitions from PD 10’s Multidimensional Diagram? Also, can it generate Oracle’s Materialized View deinition from the Multidimensional Diagram? …I have an Oracle 10gr2 PDM in which I have materialized views. I have several views that are built from the materialized views. Why doesn’t PowerDesigner show dependencies for the materialized views? Thanks PowerDesigner Version: 12.5 …Hello. It seems that there is a bug during generation by using the generate function tools. The facts : I have 2 materialized views : MV1 : SELECT Computed3.KCTTHEME, CAST(Computed3.KCTSTATISTIQUE AS VARCHAR2(50)) KCTSTATISTIQUE, CAST(Computed3.KCTGRANDEUR AS VARCHAR2(50)) KCTGRANDEUR, Computed3.KCTTEMPOREL, Computed3.ENTITE, Computed3.CENTTRS, CAST(Computed3.KCTENTRYCODE AS VARCHAR2(50)) KCTENTRYCODE, Computed3.ENTRYDATE, Sum(Computed3.NVALUE) AS NVALUE FROM ( SELECT bla bla … ); MV2 : SELECT ‘kctStatinstruc…Hello, reading through EBF coverletter I’ve found something about ‘materialized views’. Are they really supported in ASE 11.9.2? If yes,how do I create them? If not,will they be supported in 12.5? — WBR,Mike This is a multi-part message in MIME format. ————–CCAD0FB91D632F9699585D16 Content-Type: text/plain; charset=us-ascii Content-Transfer-Encoding: 7bit Mike Karmyshev wrote: > > Hello, > reading through EBF coverletter I’ve found something about ‘materialized > views’. > Are they really supported in ASE 11.9…Hi, Is anyone out there doing ASE to ASE replication using SQL remote? If you are have created your own version of the SSEXTRACT utility to set up a replicant database in ASE rather than ASA? Or does anyone know if SYBASE has created a version SSEXTRACT for use with ASE to ASE replication? Thanks in advance Doug Trainer Hi Douglas, I thought the SQL Remote Replication support in ASE11.5 was meant for a consolidate-database only (by design)! At least that was what Sybase said when they announced support for SQL Remote technology support in ASE11.5. I will be happy to…Reading a McChrystal thread elsewhere, I came across someone quoting a US general http://en.wikipedia.org/wiki/Smedley_Butler > I spent 33 years and four months in active military service and > during that period I spent most of my time as a high class thug for > Big Business, for Wall Street and the bankers. In short, I was a > racketeer, a gangster for capitalism. I helped make Mexico and > especially Tampico safe for American oil interests in 1914. I helped > make Haiti and Cuba a decent place for the National City Bank boys to > collect revenues in. I …I recently got a new job at a new company. My previous experience had all been with Oracle DB’s but the new place uses Sql Server 2k. I have a few general questions about how to do stuff in SqlServer… 1) how do I create a sequence and a trigger? 2) I know SqlServer probably doesnt have anything like Packages, so are procedures local to schemas? and if so, are schema’s nestable in any way? 3) Can anyone reccomend a good book on T-SQL? 1) how do I create a sequence and a trigger?SQL Server does not have sequence, but you can create Identity auto-number per table. Yo…Question: Do we have “materialized view” in ASE 15 yet? Thanks, Sid. On Apr 24, 4:36=A0pm, sid wrote: > Question: > > Do we have “materialized view” in ASE 15 yet? No, I don’t think so. What functionality would you want it for? Michael …Could it be possible to take scripts from ASA and load them in an ASE Any information is welcome jean-fran�ois ASA supports a fairly large sub-set of Transact-SQL, so if you write your stored procedures and triggers in ASA using T-SQL, you should be able to create scripts that will run against both ASE and ASA. If you’re planning to do this though, I would suggest developing your database schema against ASA, since everything you write in T-SQL in ASA will be supported on ASE, but the reverse is not true. Check out the section in the ASA documentation entitled “Tra…I found the following text in the help (REFRESH MATERIALIZED VIEW statement) “Any open cursors that the reference materialized view are closed during a refresh. ” If you are in a multi-user environment and make use of materialized views, how do you decide when the materialized view can be refreshen without closing the cursor from other users? How to decide if there are any open cursors? Try locking the table in exclusive mode maybe? Check out for shared intent locks on the table [see sa_locks() stored procedure]? Doe that help out at all? wrote i…We are trying to migrate a server running on Windows NT to a Solaris machine. The NT box is running ASE 11.5 and the Solaris box is running 12.5. So far, the only way we have came up with to migrate the data is bcp. The problem is we estimate this to be probably about a 6-7 hour process. Our shop runs 24*7*365 and unfortunately downtime is not something we get. In the past when we have migrated servers, we have used warm-standby which has worked really nicely. But in this case we can’t run dump and load to populate the database. Has anyone been able to migrate an NT box to a…I have installed the Sybase OCS 15.02 over 12.5.4 and have copied the DLL’s as advised by sybase. However, when I try to open a table in an ASE 15 database in Powerbuilder (or Infomaker), the following error is displayed. ct_fetch(): user api layer : internal common library error: The bind of result set item 4 failed due to a syntax error in the source data. I can see the table columns thereafter, but other information such as indices etc is not available. Somehow there is clash between 12.5.4 and 15.02. Does anyone know a solution ? Thanks in advance bryan …
What are Materialized Views? A 5 Minute Introduction
Materialized View vs View
So, what are the major differences between a view and materialized view, and why should you use one over the other?
View is a very particular type of virtual table that is created using the command “Create View.” It contains all data obtained from a relevant query expression.
If you’re working with one or even multiple base tables or views, you can use this to create a View. You can also query your View in the same way you would use one of those base tables.
The key thing to understand is that a View is always computed every time it is used or accessed in any way. Because of this, you’re always getting the most updated data in any View that you query.
If you make any type of update to the content in a View, it will always be “pushed back” and updated in the original table. Likewise, the reverse is also true: any changes that are made to the original base table are instantly reflected in the View.
What this means, however, is that the performance of a View will always be slower than that of a materialized view. The major advantage is that a View doesn’t actually require storage space. You can also have total control over which users can or cannot view sensitive information within the database itself.
A materialized view, on the other hand, is a physical copy of those original base tables. Think of it more like a photograph of the original base table. The key difference is that a materialized view will not be updated every time it is interacted with.
If you don’t go to the trouble of updating it manually, you can do so with the help of certain pre-defined triggers.
The upshot is that a materialized view will always respond faster to a query than a traditional View would, but you could also be retrieving stale data if you’re not careful.
Materialized View vs Table
The answer to that question within the context of environments like Oracle or MySQL ultimately comes down to a concept called “dynamic query rewriting.” It’s key to understand that a materialized view won’t properly define the relationships between data and the original base table. It will also allow users to pre-compute expensive joins and aggregations as well.
Likewise, the materialized view will also have certain options that can be used to automatically refresh the data being queried. Not only will this ultimately improve the ease of maintenance, but it will also go a long way towards improving efficiency as well because those changes can be tracked by row.
To put it another way, the materialized view will always be in sync with the “real data” in the table. You don’t actually have to go out of your way to do anything because, at that point, the database will do it for you.
키워드에 대한 정보 sybase materialized view
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이 기사는 인터넷의 다양한 출처에서 편집되었습니다. 이 기사가 유용했기를 바랍니다. 이 기사가 유용하다고 생각되면 공유하십시오. 매우 감사합니다!
사람들이 주제에 대해 자주 검색하는 키워드 What is a Materialized View?
- What is a Materialized view?
- Materialized view overview
- view vs materialized view
- Materialized Query table
- Indexed View
- indexed views
What #is #a #Materialized #View?
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