Performance Hot Spots Framework for Software

“Good performance is about good design, not good hardware.” — Bob Wilcox

While working on performance projects at Microsoft, I developed a software Performance Hot Spots Framework that we used to organize and prioritize software performance and scalability issues.

I employed this software Performance Hot Spots Framework to organize performance guidelines and checklists for optimal software performance.

Additionally, I leveraged the Performance Hot Spots Framework to establish evaluation criteria that assisted in identifying key performance decisions that could have a significant impact.

By using this Performance Hot Spots Framework to chunk up software performance challenges, I was able to create an Agile methodology for software performance.

Performance “Hot Spots” (Categories)

We found that we could organize the majority of our performance principles, patterns and practices using the following buckets:

Category	Key Considerations
Caching	Per user, application-wide, data volatility
Communication	Transport mechanism, boundaries, remote interface design, round trips, serialization, bandwidth
Coupling and Cohesion	Loose coupling, high cohesion among components and layers
Concurrency	Transactions, locks, threading, queuing
Data Access	Schema design; Paging; Hierarchies; Indexes; Amount of data; Round trips.
Data Structures / Algorithms	Choice of algorithm, Arrays vs. collections
Resource Management	Allocating, creating, destroying, pooling
State Management	Per user, application-wide, persistence, location

Threats Organized by the Performance Hot Spots Framework

With the Performance Hot Spot Framework, it’s easy to walk the categories and think of potential performance problems.

Here’s a list of potential software performance problems, organized by the Performance Hot Spot Framework:

Category	Threats
Caching	Round trips to data store for every single user request, increased load on the data store Increased client response time, reduced throughput, and increased server resource utilization Increased memory consumption, resulting in reduced performance, cache misses, and increased data store access Frequently changing data requires frequent expiration of cache, resulting in excess usage of CPU, memory, and network resources With inappropriate expiration policies or scavenging mechanisms, your application serves stale data This means that the cache in the servers in the farm is not the same and can lead to improper functional behavior of the application.
Communication	Requires multiple round trips to perform a single operation By sending more data than is required, you increase serialization overhead and network latency Boundary costs include security checks, thread switches, and serialization
Concurrency	Stalls the application, and reduces response time and throughput Stalls the application, and leads to queued requests and timeouts Additional processor and memory overhead due to context switching and thread management overhead Causes increased contention and reduced concurrency Poor choice of isolation levels results in contention, long wait time, timeouts, and deadlocks
Coupling / Cohesion	Mixing functionally different logic (such as presentation and business) without clear, logical partitioning limits scalability options Chatty interfaces lead to multiple round trips
Data Access	Increased database server processing Reduced throughput Increased network bandwidth consumption Delayed response times Increased client and server load Increased garbage collection overhead Increased processing effort required Inefficient queries or fetching all the data to display a portion is an unnecessary cost, in terms of server resources and performance Creates unnecessary load on the database server Failure to meet performance objectives and exceeding budget allocations
Data Structures / Algorithms	Reduced efficiency; overly complex code Reduced efficiency; overly complex code Passing value type to reference type causing boxing and unboxingCaching Decide overhead, causing performance hit Complete scan of all the content in the data structure, resulting in slow performance Undetected bottlenecks due to inefficient code.
Exception Management	Round trips to servers and expensive calls Expensive compared to returning enumeration or Boolean values Increased inefficiency Adds to performance overhead and can conceal information unnecessarily
Resource Management	Can result in creating many instances of the resources along with its connection overhead Increase in overhead cost affects the response time of the application; Not releasing (or delaying the release of) shared resources, such as connections, leads to resource drain on the server and limits scalability Retrieving large amounts of data from the resource increases the time taken to service the request, as well as network latency This should be avoided, especially on low bandwidth access, because it affects response time Increase in time spent on the server also affects response time as concurrent users increase Leads to resource shortages and increased memory consumption; both of these affect scalability Large numbers of clients can cause resource starvation and overload the server.
State Management	Holds server resources and can cause server affinity, which reduces scalability options Limits scalability due to server affinity Increased server resource utilization Limited server scalability In-process and local state stored on the Web server limits the ability of the Web application to run in a Web farm. Large amounts of state maintained in memory also create memory pressure on the server Increased server resource utilization, and increased time for state storage and retrieval Inappropriate timeout values result in sessions consuming and holding server resources for longer than necessary.

 

Vulnerabilities Organized by the Performance Hot Spots Framework

You can also use the Performance Hot Spots Framework to identify common mistakes that lead to the performance problems above.

Here’s a list of common design mistakes we find in applications:

Category	Vulnerabilities
Caching	Not using caching when you can Updating your cache more frequently than you need to; Caching the inappropriate form of data Caching volatile or user – specific data Holding cache data for prolonged periods; Not having a cache synchronization mechanism in Web farm
Communication	Chatty interfaces Sending more data than you need Ignoring boundary costs
Concurrency	Blocking calls Nongranular locks Misusing threads Holding onto locks longer than necessary Inappropriate isolation levels
Coupling / Cohesion	Not using logical layers Object-based communication across boundaries
Data Access	Poor schema design; Failure to page large result sets Exposing inefficient object hierarchies when simpler would do Inefficient queries or fetching all the data Poor indexes or stale index statistics; Failure to evaluate the processing cost on your database server and your application.
Data Structures / Algorithms	Choosing a collection without evaluating your needs (size, adding, deleting, updating); Using the wrong collection for a given task Excessive type conversion Inefficient lookups Not measuring the cost of your data structures or algorithms in your actual scenarios
Exception Management	Poor client code validations Exceptions as a method of controlling regular application flow Throwing and catching too many exceptions Catching exceptions unnecessarily
Resource Management	Not pooling costly resources; Holding onto shared resources Accessing or updating large amounts of data Not cleaning up properly Failing to consider how to throttle resources.
State Management	Stateful components; Use of an in-memory state store Storing state in the database or server when the client is a better choice Storing state on the server when a database is a better choice Storing more state than you need; Prolonged sessions

 

Countermeasures Organized by the Performance Hot Spots Framework

Here’s a list of common design strategies that lead to better performance:

Category	Countermeasures
Caching	Decide where to cache data; Decide what data to cache Decide the expiration policy and scavenging mechanism Decide how to load the cache data Avoid distributed coherent caches.
Communication	Choose the appropriate remote communication mechanism Design chunky interfaces Consider how to pass data between layers Minimize the amount of data sent across the wire Batch work to reduce calls over the network Reduce transitions across boundaries Consider asynchronous communication Consider message queuing Consider a “fire and forget” invocation model
Concurrency	Reduce contention by minimizing lock times Balance between coarse- and fine-grained locks Choose an appropriate transaction isolation level Avoid long-running atomic transactions
Coupling / Cohesion	Design for loose coupling Design for high cohesion Partition application functionality into logical layers Use early binding where possible Evaluate resource affinity
Data Access	Consider abstraction versus performance Consider resource throttling Consider the identities you flow to the database Separate read-only and transactional requests Avoid unnecessary data returns
Data Structures / Algorithms	Choose an appropriate data structure Pre-assign size for large dynamic growth data types Use value and reference types appropriately
Exception Management	Do not use exceptions to control regular application flow Use well-defined exception handling boundaries Structured exception handling is the preferred error handling mechanism Do not rely on error codes Only catch exceptions for a specific reason and when it is required
Resource Management	Treat threads as a shared resource Pool shared or scarce resources Acquire late, release early Consider efficient object creation and destruction Consider resource throttling
State Management	Evaluate stateful versus stateless design Consider your state store options Minimize session data Free session resources as soon as possible Avoid accessing session variables from business logic .

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