This document discusses how Analysis Services caching works and provides strategies for warming the Storage Engine cache and Formula Engine cache. It explains that the Storage Engine handles data retrieval from disk while the Formula Engine determines which data is needed for queries. Caching can improve performance but requires understanding when Analysis Services is unable to cache data. The document recommends using the CREATE CACHE statement and running regular queries to pre-populate the caches with commonly used data. Memory usage must also be considered when warming the caches.
Presented at JavaOne 2015.
JSR107, aka the Temporary Caching API for the Java Platform, has now been finalized almost 2 years ago. We've heard all about its ease of use and capabilities. But there is much left unaddressed. The good news is that the EG is looking at addressing many of the current shortcomings... But what do you do now? Go for proprietary APIs?!
Ehcache, the de facto caching API for 10 years now, has gone through a major API revamp: Ehcache3. One major theme, beyond its usual ease of use, was JSR107. Natively integrating it, but also looking beyond. With close to no API tie-ins, Ehcache3 lets you extend the JSR107 API transparently to go beyond the specification: topology-wise: whether you want to go offheap and scale up, or scale out by clustering your caches; functionality-wise: using transactional caches, automatic resource control or even using a write-behind cache to scale out writes...
Best of all is that this isn't only minimally intrusive, it is also all free to use and available as part of the open-source Ehcache v3 that has been GA'ed earlier this year...
This presentation mentions about key concepts of Java side caching and things to consider. It also mentions about popular tools and caching in AWS and Google App Engine.
Presented at JavaOne 2015.
JSR107, aka the Temporary Caching API for the Java Platform, has now been finalized almost 2 years ago. We've heard all about its ease of use and capabilities. But there is much left unaddressed. The good news is that the EG is looking at addressing many of the current shortcomings... But what do you do now? Go for proprietary APIs?!
Ehcache, the de facto caching API for 10 years now, has gone through a major API revamp: Ehcache3. One major theme, beyond its usual ease of use, was JSR107. Natively integrating it, but also looking beyond. With close to no API tie-ins, Ehcache3 lets you extend the JSR107 API transparently to go beyond the specification: topology-wise: whether you want to go offheap and scale up, or scale out by clustering your caches; functionality-wise: using transactional caches, automatic resource control or even using a write-behind cache to scale out writes...
Best of all is that this isn't only minimally intrusive, it is also all free to use and available as part of the open-source Ehcache v3 that has been GA'ed earlier this year...
This presentation mentions about key concepts of Java side caching and things to consider. It also mentions about popular tools and caching in AWS and Google App Engine.
Database Smart Flash Cache. This feature is available on Solaris and Oracle Enterprise Linux and allows customers to increase the effective size of the Oracle database buffer cache without adding more main memory to the system. For transaction-based workloads, Oracle database blocks are normally loaded into a dedicated shared memory area in main memory called the System Global Area (SGA). Database Smart Flash Cache allows the database buffer cache to be expanded beyond the SGA in main memory to a second level cache on flash memory.
How to fix IO problems for faster SQL Server performanceSolarWinds
How do you determine the impact of I/O on poor performance? Learn the fundamentals of SQL Server storage and how it impacts performance, including:
*the difference between latency, throughput, IOPS, and how they relate
*performance characteristics of different storage solutions
*techniques for analyzing storage subsystem performance
*new features in SolarWinds Database Performance Analyzer that will help you more accurately pinpoint and resolve I/O issue
Database Smart Flash Cache. This feature is available on Solaris and Oracle Enterprise Linux and allows customers to increase the effective size of the Oracle database buffer cache without adding more main memory to the system. For transaction-based workloads, Oracle database blocks are normally loaded into a dedicated shared memory area in main memory called the System Global Area (SGA). Database Smart Flash Cache allows the database buffer cache to be expanded beyond the SGA in main memory to a second level cache on flash memory.
How to fix IO problems for faster SQL Server performanceSolarWinds
How do you determine the impact of I/O on poor performance? Learn the fundamentals of SQL Server storage and how it impacts performance, including:
*the difference between latency, throughput, IOPS, and how they relate
*performance characteristics of different storage solutions
*techniques for analyzing storage subsystem performance
*new features in SolarWinds Database Performance Analyzer that will help you more accurately pinpoint and resolve I/O issue
[Hanoi-August 13] Tech Talk on Caching SolutionsITviec
ITviec Tech Talk
Hanoi, 24 August 2013
Topic: Caching Solutions
Speaker: Mr. Hoang Tran from Niteco
For full report of the talk: http://blog.itviec.com/2013/08/caching-solutions-response-time-niteco/
Using Query Store to Understand and Control Query PerformanceGrant Fritchey
Understanding which queries are causing the most difficulty in your systems can be a challenge. Then, fixing those problematic queries is yet another challenge. The Query Store, running in SQL Server and Azure SQL Database, can help you identify problematic queries, and it can help you fix their performance. This session will show you the various data points that Query Store collects that will help you identify the queries that are behaving badly. In addition, this session will show you the different mechanisms within Query Store that can help you fix poorly performing queries. We'll cover Query Store functionality from SQL Server 2016 through to the new stuff in SQL Server 2022. Along the way we'll cover various settings that help you control how Query Store behaves. Query Store is something you can put to work immediately in your own environments that will help you improve performance right away.
Geek Sync I Need for Speed: In-Memory Databases in Oracle and SQL ServerIDERA Software
You can watch the replay for this Geek Sync webcast in the IDERA Resource Center: http://ow.ly/S6MG50A5ok5
Microsoft introduced IN-MEMORY OLTP, widely referred to as “Hekaton” in SQL Server 2014. Hekaton allows for the creation of fully transactionally consistent memory-resident tables designed for high concurrency and no blocking. With SQL 2016, many of the original restrictions and limitations of this feature have been reduced. IDERA’s Vicky Harp will give an overview of this feature, including how to compile T-SQL code into machine code for an even greater performance boost.
There’s also been a lot of buzz about Oracle 12c’s new IN-MEMORY COLUMN STORE. Oracle ACE Bert Scalzo will cover this new feature, how it works, it’s benefits, scripts to measure/monitor it and more. He will also touch on performance observations from benchmarking this new feature against more traditional SGA memory allocations plus Oracle 11g R2’s Database Smart Flash Cache. All findings, scripts and conclusions from this exercise will be shared. In addition, two very popular database benchmarking tools will be highlighted.
Investigate SQL Server Memory Like Sherlock HolmesRichard Douglas
Memory is one part of the holy trinity of resources consumed by SQL Server, the others being CPU and disk. Most people know how to look at disk latency and throughput and then take remedial measures to fix those issues. But what about memory issues?
In this session, you will learn how SQL Server uses memory and various caches, how to gauge memory pressure, and how to address the significant problems it can cause.
You will leave with a much clearer understanding of how to monitor and manage memory consumption within SQL Server using native Dynamic Management Objects.
Find Site Performance from the server to WordPress. A look at how some good performance gains can be made in tuning MySQL and APC and getting the most of out W3 Total Cache.
SQL Server Wait Types Everyone Should KnowDean Richards
Many people use wait types for performance tuning, but do not know what some of the most common ones indicate. This presentation will go into details about the top 8 wait types I see at the customers I work with. It will provide wait descriptions as well as solutions.
Most mid-sized Django websites thrive by relying on memcached. Though what happens when basic memcached is not enough? And how can one identify when the caching architecture is becoming a bottleneck? We'll cover the problems we've encountered and solutions we've put in place.
In today’s systems , the time it takes to bring data to the end-user can be very long, especially under heavy load. An application can often increase performance by using an appropriate caching system. There are many caching level that you can use in our application today : CDN, In-Memory/Local Cache, Distributed Cache, Outut Cache, Browser Cache, Html Cache
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
1. Agenda
• How Analysis Services caching works
• When and why Analysis Services can’t cache
data
• Warming the Storage Engine cache with the
CREATE CACHE statement
• Warming the Formula Engine cache by
running queries
• Automating cache warming
2. How Analysis Services answers queries
Formula Engine
works out what data is
needed for each query,
and requests it from the
Storage Engine
Storage Engine
handles retrieval of
raw data from disk,
and any aggregation
required
MDX Query In Cellset Out
Query Subcube
Requests
Cache
Cache
Disk
3. Types of Analysis Services cache
• Analysis Services can therefore cache two types of value:
– Values returned by the Storage Engine
• ‘Raw’ measure values, one cache per measure group
• Dimension data, one cache per dimension
– Values returned by the Formula Engine
• Numeric values only – strings can’t be cached
• All SE caches have the same structure, known as the data
cache registry
• The FE can also store values in this structure if calculations
are evaluated in bulk mode
• The FE uses a different structure, the flat cache, for
calculations evaluated in cell-by-cell mode
4. Storage Engine Cache
• Data in the data cache registry is held in subcubes, ie
data at a common granularity
• Subcubes may not contain an entire granularity – they
may be filtered
• SE cache data can be aggregated to answer queries
– Except when the measure data itself cannot be
aggregated, for example with distinct count measures or
many-to-many
• Sometimes more data is fetched into cache than is
necessary for the query – this is known as ‘prefetching’
– Usually good for performance, but can cause problems
• Arbitrary-shaped subcubes cannot be cached
5. Formula Engine Cache Scopes
• There are three different ‘scopes’ or lifetimes of a FE cache:
– Query – for calculations defined in the WITH clause of a query,
the FE values can only be cached for the lifetime of the query
– Session – for calculations defined for a session, using the
CREATE MEMBER statement executed on the client, FE values
can only be cached for the lifetime of a session
– Global – for calculations defined in the cube’s MDX Script, FE
values can be cached until either
• Any kind of cube processing takes place
• A ClearCache XMLA command is executed
• Writeback is committed
• Global scope is best from a performance point of view!
6. Cache Sharing
• Values stored in the SE cache can always be shared
between all users
• Values stored in the FE cache can be shared between
users, except when:
– Stored in Query or Session-scoped caches
– Users belong to roles with different dimensions security
permissions
• Note: dynamic security always prevents cache sharing
• Calculations evaluated in bulk mode cannot reference
values stored in the FE flat cache
• Calculations evaluated in cell-by-cell mode cannot
reference values stored in the FE data cache registry
7. Forcing Query-scoping
• In certain circumstances, SSAS uses query-scoped FE
caches when you would expect it to use global scope
• These are:
– Calculations that use the Username or LookupCube
functions
– Calculations use non-deterministic functions such as Now()
or any SSAS stored procedures
– Queries that use subselects
– When any calculated member is defined in the WITH
clause, whether it is referenced or not in the query
– When cell security is used
8. Warming the SE cache
• Considerations for warming the SE cache:
– We want to avoid cache fragmentation, for example having
one unfiltered subcube cached rather than multiple
filtered subcubes
– It is possible to overfill the cache – the SE will stop looking
in the cache after it has searched 1000 subcubes
– We want to cache lower rather than higher granularities,
since the latter can be aggregated from the former in
memory
– We need a way of working out which granularities are
useful
9. Warming the SE cache
• We can warm the SE cache by using either:
– WITH CACHE, to warm the cache for a single query – not
very useful
– The CREATE CACHE command
• Remember that building aggregations is often a better
alternative to warming the SE cache
• But in some cases you can’t build aggregations – for example
when there are many-to-many relationships
10. CREATE CACHE
• Example CREATE CACHE statement:
CREATE CACHE
FOR [Adventure Works] AS
'({[Measures].[Internet Sales Amount]},
{[Date].[Date].[Date].MEMBERS},
{[Product].[Category].[Category].MEMBERS})'
11. Which subcubes should I cache?
• The Query Subcube and Query Subcube Verbose
events in Profiler show the subcubes requested
from the SE by the FE
• This is also the information stored in the SSAS
query log, stored in SQL Server
• Analyse this data manually and find the most
commonly-requested, lower-granularity subcubes
• Maybe also query the Query Log, or a Profiler
trace saved to SQL Server, to find other subcubes
– perhaps for queries that have been run recently
12. Warming the FE cache
• First, tune your calculations! Ensure use of bulk mode
where possible
• The only way to warm the FE cache is to run MDX
queries containing calculations
• Remember, these queries must not:
– Include a WITH clause
– Subselects
• Also, no point trying to cache calculations whose
values cannot be cached
• And think about how security can impact cache usage
13. Queries to warm the FE Cache
• Again, it is worth manually constructing some MDX queries yourself to
warm the FE cache
• Also, running regularly-used queries (for example those used in SSRS
reports) can be a good idea
• Can easily collect the queries your users are running by running a
Profiler trace, then saving that trace to SQL Server or a .trc file
– The Query Begin and Query End events contain the MDX query
– Need to filter out those with a WITH clause etc
– Watch out for parameterisation (eg SSRS)
– Watch out for use of session sets and calculations (eg Excel 2003)
– Watch out for queries that slice by Time, where the actual slicer
used may change regularly
– Think about the impact of dimension security too
14. Memory considerations
• SSAS caching can use a lot of memory!
• The cache will keep growing until SSAS thinks it is running
out of memory:
– When memory usage exceeds the % of available system
memory specified in the LowMemoryLimit property, data will be
dropped from cache
– When it exceeds the % specified in the TotalMemoryLimit
property, all data will be dropped from cache
– We therefore don’t want to exceed the LowMemoryLimit
– We also want to avoid paging
– We need to leave space for caching real user queries
• The FE flat cache is limited to 10% of the TotalMemoryLimit
– If it grows bigger than that, it is completely emptied
15. Automating Cache Warming
• We should perform cache-warming after cube
processing has taken place
• Remember – it may take a long time! It should not
overlap/interfere with real users querying
• We can automate it a number of different ways:
– Running SSRS reports on a data-driven subscription
– Using the ascmd.exe utility
– Building your own SSIS package – the best solution for
overall flexibility.
• Either fetch queries from a SQL Server table
• Or from a Profiler .trc file using the Konesans Trace File Source
component
16. Summary
• Clearly a lot of problems to watch out for!
• However, some cache-warming (however
inefficient) is often better than none at all
• A perfectly-tuned cube would have little need for
cache-warming, but...
– Some performance problems we just don’t know
about
– Some we may not be able to fix (eg with complex
calculations, hardware limitations)
– Cache warming is likely to have some positive impact
in these cases – maybe lots, maybe not much