Performance crime: Params

You might heard params are evil. Have you ever seen the performance bill for that?

How to find out where params are used?

Memory allocation report shows all allocations, we’ll start from here:

Method with params creates an array behind the scenes, so hunting for object[]:

IL code proves sad truth – array is always created:

Even though there is a faster Concat with 4 arguments, it is not picked due to type mismatch. The similar issue takes place for Item.GetUniqueId():

How faster could it be without params?

2.7 times faster by simply calling .ToString prior combining values. Impressive, huh?

        public void Stock()
            var data = _item.InnerData;
            for (int i = 0; i < N; i++)
                var clone = new Item(_id, data, database);
                var uniqueId = clone.GetUniqueId();

        public void NoBoxing()
            var data = _item.InnerData;
            for (int i = 0; i < N; i++)
                var clone = new Item(_id, data, database);
                var uniqueId = string.Concat(clone.Database.Name, clone.ID.ToString(), clone.Language.Name, clone.Version.ToString());                

2.4 times less memory used (omitting justCopyItem from both runs: 27.5 vs 11.47 MB)


Params could make your program slower out of nothing. Luckily, there are automated solutions (like Roslyn Clr Heap Allocation Analyzer) that help detecting these kind of issues in early development stages.

Although modern compilers produce smarter code that does not involve array creation in this case, new language features (string interpolation) brings new challenges.

Reckless interlocked operations

Let’s start with a guess – what is the most CPU-heavy operation in Sitecore?

Answer: Performance counters initialization:

Yes, that’s right, those performance counters which are powered by OS and super fast.

Although Sitecore counter init implementation does not look offensive (volatile read & interlocked), counters are everywhere (cache lookup / access / object creation):

Considering server concurrently runs N threads & each updates memory == huge impact.

Benchmark time: VTune time

Intel VTune shows performance counters init code is translated into lock cmpxchg:

if (initialized != 1 && Thread.VolatileRead(ref initialized) != 1 && Interlocked.CompareExchange(ref initializingCounter, 1, 0) != 1 && AllowConnect)

Sync between all the cores & writes on each go to the memory leads to almost five times lower processor efficiency [CPI] on the surface (theoretical x5 slow down):

In simple words: Intel I7 with 8 cores performs as 2014 Intel 2-core I3.

How big is the win without interlocked?

Changing the conditions order (counters are never initialized if disabled in config) improves processor efficiency 15 times (4,6 vs 0.46):

                if (AllowConnect == false)

                if (initialized != 1 && Thread.VolatileRead(ref initialized) != 1 && Interlocked.CompareExchange(ref initializingCounter, 1, 0) != 1)

No memory writes == no memory-bound bottleneck:


The execution time dropped 22 times (5.8 -> 0.27 sec) just by preventing reckless interlocked operations on each go.

Why should an upper limit exist for every saved bit

Case study: polluted reports shows how system can be polluted with dummy data.

Saving data (even HTTP referer) without validation can contaminate system as well:

	,JSON_QUERY(FacetData,'$.Referrers') as [Referrers]
	, DATALENGTH(JSON_QUERY(FacetData,'$.Referrers')) as [ReferrerSize]
	AND CHARINDEX('"Referrers":["', FacetData) > 0
  ORDER BY [ReferrerSize] DESC

The results show astonishing 28KB for storing single value:

Next time you see Analytics shards worth 600 GB – recall this post.

Can configuration be trusted?

Configuration can go nuts: CMS 8.1 setup needs 200+ files to be edited by hand:

Neither connection strings, nor custom features are in included yet; things get more complicated in reality.

Lifebuoy: configuration roles

A few enthusiasts simplified configuration task solely to in 9+ series:

<settings role:require="Standalone" environment:require="Production">    
    <setting name="X" set:value="Y"/>

Next era: containers and configuration

Docker eliminates the need in 14+ steps to run the Sitecore. All you have to do is to cook an image of your changes on top of vendor image (like layered cake) & play it anywhere.

Dilemma: the same image in different environments = same files; but still have different connection strings / features …. How?

Environment variables

Latest Sitecore is capable of fetching configuration values from environment variables:

Amazingly, there is no way out-of-the-box to see running values!

Showconfig shows only sitecore node, while role:define, search:define live inside web.config. Moreover, web.config will have an outdated value when environment variable was picked.

Baking solution to see running values

Let’s bake the page to show all roles/definitions, including custom ones:

  • Sitecore role (Standalone, CD, CM..)
  • Indexing engine used (SOLR, AzureSearch..)
  • Any custom layer (f.e. security:define or myproject.environment:define)
    public class ShowRoles : AdminPage
        private readonly IConfigurationRulesContext _configRulesContext;

        public ShowRoles() { /* Dummy to make ASP.NET happy. */ }

        public ShowRoles(IConfigurationRulesContext configRulesContext) => _configRulesContext = configRulesContext;

        protected void Page_Load(object sender, EventArgs e)
                Response.Write("<h3>Sitecore rule definitions</h3>");
                var names = _configRulesContext.GetRuleDefinitionNames();
                foreach (var name in names)
                    var values = _configRulesContext.GetRuleDefinitionValue(name);
                    Write(name, values);

        private void Write(string group, IReadOnlyCollection<string> elements)
            elements = (elements ?? Array.Empty<string>()).Where(element => !string.IsNullOrWhiteSpace(element))
            if (elements.Any())
                foreach (var element in elements)
                Response.Write("<p>No elements in the group.</p>");

Demo compose file looks as:


Gotcha: Not all settings were applied

myproject.environment is not applied; environment-specific config is always ON:

Container variables are there, though:

Are environment variables visible to Sitecore process?

                Response.Write("<h3>Environment variables</h3>");
                var environmentVariables = Environment.GetEnvironmentVariables();
                foreach (string variable in environmentVariables.Keys)
                    var values = new[] { environmentVariables[variable] as string };
                    Write(variable, values);

Yes, the missing setting is exposed as environment variable:

How come value is not picked by Sitecore?

Short answer – that is life. Adding the dummy into web.config makes variable be picked:

Adding a key with dummy value
Using file explorer to upload a modified web.config
Page output shows custom:define now

The current implementation requires app:key to present in config to be replaced in runtime. And yes, there is no way to check if it is picked 😉


A lack of configuration assemble traceability leads to huge investigation effort when things do not work well. File-based configuration can no longer be trusted in containerized deployments; at the same time there is no source of truth to verify its correctness.

How often the site is visited from specific place?

Can “site visit frequency from specific place (or better, certain company office)” be just a query away? The needed analytics data is already collected by Sitecore, hence data mining could roughly be:

  1. Figure out area postal code (or reverse it by IP using any reverse IP lookup)
  2. Find all contacts that have the same details postal code (or by other field criteria)
  3. Locate visits done by the contacts
  4. Aggregate the number of pages in each visit to understand the length of their journey

The IP address in our demo belongs to Dnipro with 49000 postal code. It is recorded by Sitecore Analytics in following manner:

The GeoIP data is a part of InteractionsCache facet that belongs to contact; we could find all the contacts from postal code/city/(any condition from picture above) by query:

DECLARE @location NVARCHAR(20) = '49000';
DECLARE @LocationUtcShift INT = 2; 
DECLARE @MinInteractionsThreshold INT = 6;
DECLARE @ShardsCount INT = 2;

WITH [ContactsInTheArea] AS(
		DISTINCT(cf.ContactId) AS [ContactId]
	FROM [xdb_collection].[ContactFacets] cf
		CROSS APPLY OPENJSON([FacetData], '$.PostalCodes')
			WITH ([City] NVARCHAR(100) '$')
	AND ISJSON(FacetData) = 1 
	AND [City] = @location)
SELECT COUNT(1) AS [Unique browser sessions] FROM [ContactsInTheArea]

The next step is to locate all the interactions recorded in system:

[InteractionsFromTheArea] AS(
	DATEADD (HOUR, @LocationUtcShift, i.StartDateTime) AS [StartDateTime],
	DATEADD (HOUR, @LocationUtcShift, i.EndDateTime) AS [EndDateTime],
	Pages = (
			FROM OPENJSON([Events])
				WITH ([Event] NVARCHAR(100) '$."@odata.type"') 
			WHERE [Event] = '#Sitecore.XConnect.Collection.Model.PageViewEvent')
	FROM [xdb_collection].Interactions i
		INNER JOIN [ContactsInTheArea] d 
		ON d.[ContactId] = i.ContactId)
SELECT * FROM [InteractionsFromTheArea]

We found all the recorded interactions performed from the location we originally set. The last step is to aggregate statistics per day:

	CAST (i.StartDateTime AS DATE) AS [Session Time],
	COUNT(1) AS [Test Sessions],	
	CAST(ROUND(AVG(CAST(Pages AS FLOAT)), 2) AS NUMERIC(36,2)) AS [Avg. pages viewed]
FROM [InteractionsFromTheArea] i
	GROUP BY CAST (i.StartDateTime AS DATE)
	HAVING COUNT(1) > (@MinInteractionsThreshold / @ShardsCount)
	ORDER BY [Session Time] DESC

The last query answers how often our site was visited in the area that belongs to the postal code/(company name owing the IP address):


A daily statistics of interactions (and their quality) originated from area is a query away, impressive? Since we operated on one shard out of N, the results are to be multiplied by N to get complete picture.

The report is built by burning CPU to parse raw JSON on each go (the more data = the more CPU spent). A lack of data normalization is a price to pay for flexibility (possibility to track/store custom info) that introduces a need of reducing/extracting/aggregating data (constantly adjust report data to reflect data change) and storing into query-friendly format.

Case study: polluted reports


Analytics reports have suspicious statistics with lower conversion rates compared to other systems. Can we find out why?

It seem that healthy data is diluted with junk/empty interactions with no value. We assume robot/crawlers activity gets recorded. Is there any OOB protection in Sitecore?

Filter out robots by user agents

Sitecore blacklists robots via a list of user agents defined in the config:


User agents that should be excluded rain or shine

Theoretically, zero interactions with these user agents should be recorded, right? Well, I do not blog about straightforward tasks. We could check the actual number of robots via huge SQL composed by replacing line break character with ',':

But still, 20% of all contacts (that have interactions) report to have robot user agents. Could it be due to the fact Sitecore uses case-sensitive match for robots, while SQL column has case-insensitive SQL_Latin1_General_CP1_CI_AS collation? While that can easily happen in your case, a cause is different in our case study:

Let’s leave this question unanswered (for now) and focus on what robot is?

How to identify robot by behavior?

Page crawler requests pages one by one without persisting any cookies – it will not have interactions with more than one page. We could try to find user agents that do not have more than one page recorded:

WITH PagesInInteractions AS(
	SELECT Pages = (
			FROM OPENJSON([Events])
				WITH ([Event] NVARCHAR(100) '$."@odata.type"') 
			WHERE [Event] = '#Sitecore.XConnect.Collection.Model.PageViewEvent'),
FROM [xdb_collection].[Interactions])
	COUNT(1) AS Hits,
	DATEDIFF(DAY, MIN(Created), MAX(LastModified)) AS [DaysBetween],  
	MIN(Created) AS [Earliest],
	MAX(LastModified) AS [Latest]
FROM PagesInInteractions 
GROUP BY [UserAgent] 
	MAX(Pages) <=1 
	AND COUNT(1) > 500

This query finds unique user agents that have viewed single-page only and have over 500 interactions:

20% of total interactions recorded system-wide belong to user agents that do not have over 1 page in visit across 500 visits. These user agents are most likely to be added into the blacklist to stop them from being tracked.

Could contacts without interactions exist?

Although that should not happen in theory… you got it:

SELECT COUNT(DISTINCT(c.ContactId)) FROM [xdb_collection].Contacts c
LEFT JOIN  [xdb_collection].Interactions i
ON i.ContactID = c.ContactId

Our case study has 7.5 % of contacts without interactions that was caused by bug.


The query we developed to locate suspicious user agents allows us to identify robots with better accuracy in future. Unfortunately, the previously collected robot sessions would still remain in system and pollute analytics reports. Needless to say that you pay for storing useless data to your hosting vendor.

In next articles we’ll try to remove the useless data from system to recover the reports.

Unit testing in Sitecore

Sitecore became test-friendly as soon as Dependency Injection was added in with 8.2 release:

public class Dummy
    private readonly BaseItemManager _baseItemManager;

    public Dummy(BaseItemManager itemManager)
        _baseItemManager = itemManager;

    public string Foo(ID id, ID fieldID)
        // Legacy approach with static manager
        // var item = Sitecore.Data.Managers.ItemManager.GetItem(id);
        var item = _baseItemManager.GetItem(id); 
        return item[fieldID];

However, straightforward unit test would have a long arrange for Sitecore entities:

public class DummyTests
    [Theory, AutoData]
    public void Foo_Gets_ItemField(ID itemId, ID fieldId, string fieldValue)
        var itemManager = Substitute.For<BaseItemManager>();
        var database = Substitute.For<Database>();
        var item = Substitute.For<Item>(itemId, ItemData.Empty, database);
        var sut = new Dummy(itemManager);

        var actual = sut.Foo(itemId, fieldId);        

8 lines of code (>550 chars) to verify a single scenario is too much code.

How to simplify unit testing?

A big pile of solution code is typically build around:

  • Locating data by identifier (GetItem API)
  • Processing hierarchies (Children, Parent, Axes)
  • Filtering based on template
  • Locating specific bits (accessing fields)

The dream test would contain only meaningful logic without arrange hassle:

[Theory, AutoNSubstitute]
public void Foo_Gets_ItemField(FakeItem fake, [Frozen] BaseItemManager itemManager, Dummy sut, ID fieldId, string fieldValue)
    Item item = fake.WithField(fieldId, fieldValue);        

    var actual = sut.Foo(item.ID, fieldId);

Better? Let’s take a closer look what has changed so that test is only 4 lines now.

Building items via SitecoreDI.NSubstitute.Helper

Sitecore.NSubstituteUtils builds anything related to item in builder pattern:

var bond = new FakeItem()
            .WithName("Bond, James Bond")
            .WithField(FieldIDs.Code, "007")
            .WithChild(new FakeItem())

Code samples were crafted to make a learning curve as easy as it could be imagined.

To cut it short – all major item properties can be arranged by this engine.

Inject items into tests via AutoFixture

AutoFixture creates all the needed entities if taught how to:

 public class AutoNSubstituteDataAttribute : AutoDataAttribute
        public AutoNSubstituteDataAttribute()
            : base(() => new Fixture().Customize(
                new CompositeCustomization(
            new DatabaseCustomization(),
            new ItemCustomization().....))

    public class ItemCustomization : ICustomization
        public void Customize(IFixture fixture)
            fixture.Register<ID, Database, FakeItem>((id, database) => new FakeItem(id, database));
            fixture.Register<FakeItem, Item>(fake => fake.ToSitecoreItem());

    public class DatabaseCustomization : ICustomization
        public void Customize(IFixture fixture)
            fixture.Register<string, Database>(FakeUtil.FakeDatabase);

Implicit dependency warning: Sitecore.Context

Test isolation is threatened by an implicit dependency on Sitecore.Context (which is static on the surface). There are multiple solutions on the deck.

A) Clean up Sitecore.Context inner storage in each test

Context properties are based on Sitecore.Context.Items (backed either by HttpContext.Items or Thread-static) dictionary that could be cleaned before/after each test execution so that context property change effect is gone once test finishes:

public class DummyTests: IDisposable
    public DummyTests()

    public void Foo() 
        Sitecore.Context.Item = item;
    void IDisposable.Dispose() => Sitecore.Context.Items.Clear();

The approach leads to burden/hidden dependency that is a code smell.

B) Facade Sitecore.Context behind ISitecoreContext

All the custom code could use ISitecoreContext interface instead of Sitecore.Context so that all the needed dependencies become transparent:

interface ISitecoreContext 
  Item Item { get;set; }
  Database Database { get;set; }

public class SitecoreContext: ISitecoreContext
  public Item Item 
      get => Sitecore.Context.Item;
      set => Sitecore.Context.Item = value;

  public Database Database
      get => Sitecore.Context.Database;
      set => Sitecore.Context.Database = value;

The implementation can be registered as transient in DI config & consumed via constructor injection:

public class Dummy
    private readonly ISitecoreContext _sc;

    public Dummy(ISitecoreContext sc)
        _sc = sc;

    public string Foo(ID fieldID)
        var contextItem = _sc.Item;
        return contextItem[fieldID];


The approach allows writing tests in easy manner making excuse card ‘Sitecore is not testable‘ to fade into the history.

Case study: database optimization

Sitecore item is stored in 4 tables:

  1. Items: has item ID, name, parentId and the templateID item is based on
  2. SharedFields: has itemId, fieldId, and value itself
  3. UnversionedFields: has language for the value, itemId, fieldId, value
  4. VersionedFields: has version number, language, itemId, fieldId, value

The item data is read by a query that unions all the tables and uses ItemID condition:

A caching layer ensures SQL to be executed only in case data was not found in cache. There are 3 main scenarios to load item data:

  1. By item id: database.GetItem(ID) is called
  2. Children: GetChildren is called
  3. By template: during application start, initial items prefetch

Key points

  1. Individual fields are not selected by fieldId as all fields selected for item at once
  2. Items are commonly requested by ID (dominant workload)
  3. Query unions 4 tables via ItemID condition
  4. Query performs sort on database side
  5. None of the tables has primary key defined

How does the SQL Server execute query?

The default query execution plan highlights many steps to be taken to read one item:

Stock query execution plan has many nodes

Unfortunately, item-related tables do not have primary key defined so that every request does RID lookup. Since the volume of reads is far greater than the number of modifications in web and core databases, read workload optimization could be applied:

  1. Defining a primary key (non-unique) for fields table by itemID so that fields belonging to same item are stored next to each other
  2. Offloading sort operation from database to client code
  3. Use view to avoid sending long query
  4. Simplifying ItemID condition – moving away from where ID in SELECT
  5. Reduce the volume of SQL requests

Measuring the impact

Schema-change decision must be driven by data/statistics analysis, hence we’ll measure the outcome via SQL Server Profiler for default VS optimized versions:

  • Duplicate the tables with suggested improvements
  • Ensure SQL Indexes are healthy
  • Restart SQL Server
  • Request N items from database

Clustered VS Non-Clustered

Over 3 times faster thanks to clustered indexes:


SQL Server sort can be moved into the application logic to get ~50% speed up:

Not only MemortGrantInfo is 0, but also the Estimated Subtree Cost is ~47% less:

Creating SQL view

Although view does not give any boost, it hides the impl. detail on how item data is built:

CREATE VIEW [dbo].[ItemDataView]
SELECT        ItemId, [Order], Version, Language, Name, Value, TemplateID, MasterID, ParentID, Created
FROM            (SELECT        ID AS ItemId, 0 AS [Order], 0 AS Version, '' AS Language, Name, '' AS Value, TemplateID, MasterID, ParentID, Created
                          FROM            dbo.Items
                          UNION ALL
                          SELECT        ParentID AS ItemId, 1 AS [Order], 0 AS Version, '' AS Language, NULL AS Name, '' AS Expr1, NULL AS Expr2, NULL AS Expr3, ID, NULL
                          FROM            dbo.Items AS Items_Parent
                          UNION ALL
                          SELECT        ItemId, 2 AS [Order], 0 AS Version, '' AS Language, NULL AS Name, Value, FieldId, NULL AS Expr1, NULL AS Expr2, NULL
                          FROM            dbo.SharedFields
                          UNION ALL
                          SELECT        ItemId, 2 AS [Order], 0 AS Version, Language, NULL AS Name, Value, FieldId, NULL AS Expr1, NULL AS Expr2, NULL
                          FROM            dbo.UnversionedFields
                          UNION ALL
                          SELECT        ItemId, 2 AS [Order], Version, Language, NULL AS Name, Value, FieldId, NULL AS Expr1, NULL AS Expr2, NULL
                          FROM            dbo.VersionedFields) AS derivedtbl_1

Simplifying the condition to select items

The stock query would return item fields only in case item definition exists:

Query can be optimized for a mainstream scenario (item data exists) and directly stream the content of the field tables. Application may filter out rows without definitions later on:

Theoretical: Stock vs Optimized

The optimized query is 7.3 times faster than the stock:

Reduce the volume of SQL Queries

The final query streams data from tables in a fastest possible way turning request overhead (like network latency) to be top wall clock time consumer. The volume of requests could be reduced by loading not only item by ID, but also its children:

	[ItemDataView] d
	[Items] cond
  ON [d].ItemId = [cond].ID
  WHERE (cond.ID = @ID OR cond.ParentID=@ID)

Practice: Testing variations

We will load all the items from Sitecore database:

    var item = db.GetItem(Sitecore.ItemIDs.RootID);
    System.GC.Collect(System.GC.MaxGeneration,System.GCCollectionMode.Forced, true, true);    
    var ticksBefore = Sitecore.Diagnostics.HighResTimer.GetTick();    
    var items = item.Axes.GetDescendants();        
    var msTaken = Sitecore.Diagnostics.HighResTimer.GetMillisecondsSince(ticksBefore);

Results would be measured by SQL Server Profiler and aggregated to get AVG values:

View top metrics

Test combinations

  • Stock query as a base line
  • Clustered index only
  • NS: Clustered index without sort
  • +KIDS: Clustered index without sort + loading children
  • InMemory tables for all item-related tables
  • Symbiosis: InMemory for items + clustered for fields table

Results: Over 30% speedup

Results highlight clustered indexes without sort (NS) is only 10% faster

Loading children with item itself is the winner:

  • 30% faster on a local machine; even a greater win in distributed environment
  • 18% reduce number of SQL queries
  • 25% less CPU spent
  • 35% less reads

The item fetch was improved thanks to understanding how the system operates with data, thus SQL Server can handle a bigger load with no additional cost.

Performance crime: concurrent collections misuse

Concurrent collections are expected to be slower than non-concurrent counterparts due to an extra cost of synchronization across threads. Even though collections implement IEnumerable interface, it is not a usual enumeration but a moment-in-time snapshot with a few pitfalls. Let’s look at ConcurrentBag enumerable implementation:

  1. Freeze the collection by locking a top-level lock and all low-granularity locks
  2. Traverse full collection content (linked list stored in different memory locations = poor data locality) and copy all the elements into new List<T>()
  3. Unfreeze the collection so that other threads can make a copy

Not only one thread at a time can make a snapshot of the collection, but every enumeration attempt makes an allocation to produce a snapshot/copy. Should the enumerator be used often (f.e. parsing every field in SOLR search results) it would bubble in top 5 dead types in production:

Over 285K arrays are to be cleaned up by GC

The default Sitecore.ContentSearch.SolrProvider.SolrFieldMap class uses ConcurrentBag to store SolrFieldConfiguration – every GetFieldConfiguration API call ends with allocations and system-wide locking:

Concurrent bag attempts to make a snapshot, but cannot as already locked by a different thread

Leading to a bottleneck in multi-threaded environment:

Lock contention during parsing SOLR response

Despite SOLR can reply to concurrent requests in a fast manner, the result parsing on Sitecore side could slow us down.

Benchmark: Measuring stock operation performance

        public SolrFieldMapTests()
            confg = new XmlDocument();

            var factory = new TestFactory(new ComparerFactoryEx(), new ServiceProviderEx());
            _fieldMap = factory.CreateObject(confg.DocumentElement, assert: true) as SolrFieldMap;

        public const int N = 10 * 1000;        

        public void Stock_GetFieldConfiguration()
            for (int i = 0; i < N; i++)

Almost 9MB spent to locate 10K fields:

That is only for 10K elements

Not only a snapshot is produced, but also stock logic would execute sorting on each execution (instead of once during load). Can it be done better? Yes.

Solution 1: Use IConstructable interface

Since fields are defined in fieldMap section of the Sitecore Solr configuration, it seems adds are called only during object construction. IConstructable interface could have been implemented for the FieldMap to transform data from ConcurrentBag into array.

That would allow multiple threads to be executed simultaneously and save memory allocations since no snapshots are needed.

Solution 2: Use lock-free synchronization

Field configuration is added via AddTypeMatch method defined by configuration:

      <fieldMap type="Sitecore.ContentSearch.SolrProvider.SolrFieldMap, Sitecore.ContentSearch.SolrProvider">
          <!--  This element must be first  -->
          <typeMatches hint="raw:AddTypeMatch">
            <typeMatch type="System.Collections.Generic.List`1[System.Guid]" typeName="guidCollection" fieldNameFormat="{0}_sm" multiValued="true" settingType="Sitecore.ContentSearch.SolrProvider.SolrSearchFieldConfiguration, Sitecore.ContentSearch.SolrProvider" />

We could bake lock-free compare & swap solution:

private volatile SolrSearchFieldConfiguration[] availableTypes = Array.Empty<SolrSearchFieldConfiguration>();

        public void AddTypeMatch(string typeName, Type settingType, IDictionary<string, string> attributes, XmlNode configNode)
            Assert.ArgumentNotNullOrEmpty(typeName, "typeName");
            Assert.ArgumentNotNull(settingType, "settingType");
            var solrSearchFieldConfiguration = (SolrSearchFieldConfiguration)ReflectionUtility.CreateInstance(settingType, typeName, attributes, configNode);
            Assert.IsNotNull(solrSearchFieldConfiguration, $"Unable to create : {settingType}");
            typeMap[typeName] = solrSearchFieldConfiguration;

            SolrSearchFieldConfiguration[] snapshot;
            SolrSearchFieldConfiguration[] updated;
                snapshot = availableTypes; // store original pointer
                updated = new SolrSearchFieldConfiguration[snapshot.Length + 1];
                Array.Copy(snapshot, 0, updated, 0, snapshot.Length);
                updated[snapshot.Length] = solrSearchFieldConfiguration;

                updated = updated.OrderByDescending(e => e.FieldNameFormat).ToArray();
            while (Interlocked.CompareExchange(ref availableTypes, updated, snapshot) != snapshot);

public IReadOnlyCollection<SolrSearchFieldConfiguration> GetAvailableTypes() => availableTypes;

It copies the existing array content into a new one placing it next to an additional value. We’ll also do the sorting here once instead of per-call.

Since availableTypes is treated as immutable collection, it is enough only to verify array pointer value.

Benchmark: Array vs ConcurrentBag

Since updated version neither causes memory allocations, nor has sorting, nor jumps between pointers (good locality), it gets over hundred times faster with 30 times less memory allocated:


Concurrent collection usage in a wrong manner could slow down code over 100 times.

A misuse is quite hard to detect on a development machine as nothing obvious is slow. It gets even trickier to detect in case code is sitting next to out-proc resource that is always blamed for slow performance.

Performance crime: config to kill performance

Would you as a developer allow a setting that can make system 15 550 times slower?

I’ve received a few memory dumps with high CPU; each scavenges AccessResultCache:

How big is the cache so that every snapshot contains the operation?

Detecting cache size from the snapshot

A ClrMD code snippet locates objects in Sitecore.Caching.Generics.Cache namespace with cache-specific fields & showing only filled caches:

            using (DataTarget dataTarget = DataTarget.LoadCrashDump(snapshot))
                ClrInfo runtimeInfo = dataTarget.ClrVersions[0];
                ClrRuntime runtime = runtimeInfo.CreateRuntime();
                var heap = runtime.Heap;
                var stats = from o in heap.EnumerateObjects()
                            let t = heap.GetObjectType(o)
                            where t != null && t.Name.StartsWith("Sitecore.Caching.Generics.Cache")
                            let box = t.GetFieldByName("box")
                            where box != null
                            let name = o.GetStringField("name")
                            let maxSize = o.GetField<long>("maxSize")
                            let actualBox = o.GetObjectField("box")
                            let currentSize = actualBox.GetField<long>("currentSize")
                            where maxSize > 0
                            where currentSize > 0
                            let ratio = Math.Round(100 * ((double)currentSize / maxSize), 2)
                            where ratio > 40
                            orderby ratio descending, currentSize descending
                            select new
                                address = o.Address.ToString("X"),
                                currentSize = MainUtil.FormatSize(currentSize, false),
                                maxSize = MainUtil.FormatSize(maxSize, false),

                foreach (var stat in stats)

There are 5 caches that are running out of space, and AccessResultCache is one of them with 282MB running size vs 300 MB allowed:

AccessResultCache is over 280MB in size

Fetched runtime Sitecore config from snapshot proves 300 MB max size:

<setting name="Caching.AccessResultCacheSize" value="300MB"/>

Configuration to control cleanup logic

The Caching.CacheKeyIndexingEnabled.AccessResultCache setting controls how cache is scavenged:

Using indexed storage for cache keys can in certain scenarios significantly reduce the time it takes to perform partial cache clearing of the AccessResultCache. This setting is useful on large solutions where the size of this cache is very large and where partial cache clearing causes a measurable overhead.

Sitecore.Caching.AccessResultCache.IndexedCacheKeyContainer is plugged in should cache key indexing be enabled. The index is updated whenever element is added so that all elements belonging to an item can be easily located. A bit higher price for adding an element in exchange of a faster scavenge.

What is performance with different setting values?

We’ll do a series of Benchmark.NET runs to cover the scenario:

  1. Extract all in memory AccessResultCacheKeys (reuse code snipped from How much faster can it be)
  2. Mimic AccessResultCache inner store & load keys into it
  3. Trigger logic to remove element with & without index
  4. Measure how fast elements are added with & without index
  5. Measure speed for different sizes

Load keys into AccessResultCache inner storage

Default storage is ConcurrentDictionary; cleanup is a predicate for every cache key:

        private readonly ConcurrentDictionary<FasterAccessResultCacheKey, string> fullCache = new ConcurrentDictionary<FasterAccessResultCacheKey, string>();
        private readonly IndexedCacheKeyContainer fullIndex = new IndexedCacheKeyContainer();

        public AccessResultCacheCleanup()
            foreach (var key in keys)
                        cache.TryAdd(key, key.EntityId);

        private void StockRemove(ConcurrentDictionary<FasterAccessResultCacheKey, string> cache)
            var keys = cache.Keys;
            var toRemove = new List<FasterAccessResultCacheKey>();
            foreach (var key in keys)
                if (key.EntityId == keyToRemove)

            foreach (var key in toRemove)
                fullCache.TryRemove(key, out _);

        public void RemoveViaIndex(ConcurrentDictionary<FasterAccessResultCacheKey, string> cache, IndexedCacheKeyContainer index)
            var key = new FasterAccessResultCacheKey(null, null, null, keyToRemove, null, true, AccountType.Unknown, PropagationType.Unknown);

            var keys = index.GetKeysByPartialKey(key);

            foreach (var toRemove in keys)
                cache.TryRemove(toRemove, out _);


Measuring add performance

Index maintenance needs additional efforts, hence add speed should be also tested:

        public void CostOfAdd_IndexOn()
            var cache = new ConcurrentDictionary<FasterAccessResultCacheKey, string>();            
            var index = new IndexedCacheKeyContainer();
            long size = 0;
            foreach (var key in Keys)
                size += key.GetDataLength();

        public void CostOfAdd_WithoutIndex()
            var cache = new ConcurrentDictionary<FasterAccessResultCacheKey, string>();
            long size = 0;
            foreach (var key in Keys)
                cache.TryAdd(key, key.EntityId);
                size += key.GetDataLength();                

Taking into account different cache sizes

Configured 300 MB is 7 .5 times larger than default cache value (40 MB in 9.3), it makes sense to measure timings for different key count as well (58190 keys = 282 MB):

Stock configuration fits somewhere near ~8.4K entries

Understanding the results

  1. Removing element without index takes 15 550 times more
  2. An attempt to remove element costs ~400 KB memory pressure
  3. It takes 3.8 ms for a single removal on IDLE system with 4.8 GHz super CPU
    • Prod solution in cloud (constant cache hits) shall take ~4 times more
  4. Up to 8.4K entries can squeeze into OOB AccessResultCache size
    • OOB Sitecore has ~6K items in master database
    • ~25.4K items live in OOB core database
    • Each user has own access entries
  5. Adding an element into cache with index costs 15 times more


AccessResultCache is aimed to avoid repeatable CPU-intensive operations. Unfortunately, default cache size is too small so that limited number of entries can be stored at once (even less than items in master & web OOB databases). The insufficient cache size flags even on development machine:

However, defining production-ready size leads to ~15540 times higher performance penalties during cache scavenge for OOB configuration = potential for a random lag.

A single configuration change (enable cache key indexing) changes the situation drastically & brings up a few rhetorical questions:

  1. Is there any reason for AccessResultCache to be scavenged even if security field was not modified? To me – no.
  2. Any use-case to disable cache indexing in production system with large cache?
  3. What is the purpose of the switch that slows system 15.5K times?
  4. Should a system pick different strategy based on predefined size & server role?


  1. Stock Caching.AccessResultCacheSize value is too little for production, increase it at least 5 times (so that scavenge messages no longer seen in logs)
  2. Enable Caching.CacheKeyIndexingEnabled.AccessResultCache to avoid useless performance penalties during scavenge