Speaker 1: Have you ever written the link U query that worked
Speaker 1: perfectly in CRS, but when you checked the sequel it generated,
Speaker 1: you wondered, how on earth did it get to that.
Speaker 1: In this session you learn three things, in particular, how
Speaker 1: expression trees control translation, how caching shapes performance and memory use,
Speaker 1: and what to watch for when null logic doesn't behave
Speaker 1: as expected. If you've suspected there's black box magic inside
Speaker 1: Entity Framework COLL, the truth is closer to architecture than magic.
Speaker 1: Ef core uses a layered query pipeline that handles passing, translation, catching,
Speaker 1: and materialization behind the scenes. First, we'll look at how
Speaker 1: your link you becomes an expression tree, than the provider's
Speaker 1: role caching, null semantics, and finally sequel and materialization. And
Speaker 1: it all starts right at the beginning. What actually happens
Speaker 1: the moment you run a link query from link to
Speaker 1: expression trees. When you write a link query, the code
Speaker 1: isn't automatically fluent in SQL. Link is just cpar. It
Speaker 1: doesn't know anything about databases or tables. So when you
Speaker 1: add something like a weare or a select, you're really
Speaker 1: calling methods in SHI, not issuing commands to SEQL. The
Speaker 1: job of Entity framework core is to capture those calls
Speaker 1: into a form it can analyze before making any decisions
Speaker 1: about translation or execution. That capture happens through expression trees
Speaker 1: instead of immediately hitting the database. Efcre records your query
Speaker 1: as a tree of objects that describe each part. A
Speaker 1: were clause doesn't mean filter rose yet it becomes a
Speaker 1: node in the tree that says, here's a method call,
Speaker 1: here's the property being compared, and here's the constant value.
Speaker 1: At this stage, nothing has executed. EF is simply documenting
Speaker 1: intent in a structured form it can later walk through.
Speaker 1: One way to think about it is structure before meaning,
Speaker 1: just like breaking a sentence into subject and verb before
Speaker 1: attempting a translation. EF builds a tree where joints, filters, projections,
Speaker 1: and ordering are represented as notes. Only once the structure
Speaker 1: exists can sequel translation even begin. Ef core depends on
Speaker 1: expression trees as its primary mechanism to inspect link equeries
Speaker 1: before deciding how to handle them. Each clause, you write
Speaker 1: whether a joint or a filter adds new nodes to
Speaker 1: that object model. For example, a condition like c city elcutsaurser,
Speaker 1: paris arzyl become a branch with left and right parts,
Speaker 1: one pointing to the city property and one pointing to
Speaker 1: the constant string paris. By walking this structure, EF can
Speaker 1: figure out what parts of your query map to SQL
Speaker 1: and what parts don't. Behind the scenes, these trees are
Speaker 1: not abstract concepts, but actual objects in memory. Each node
Speaker 1: represents a method, call, a property, or a constant value
Speaker 1: pieces EF can inspect and categorize. This design gives EF
Speaker 1: a reliable way to pass your query without executing it.
Speaker 1: Yet internally, EF treats the tree as a model, deciding
Speaker 1: which constructs it can send to SQL and which ones
Speaker 1: it must handle in memory. This difference explains why some
Speaker 1: queries behave one way in link to objects but fail
Speaker 1: in EF. Imagine you drop a custom helper function inside
Speaker 1: a lambda filter in memory link you just runs it,
Speaker 1: but with EF, the expression tree now contains a node
Speaker 1: referring to your custom method, and EF has no circle
Speaker 1: equivalent for that method. At that point, you'll often notice
Speaker 1: a runtime error, a warning or sequel falling back to
Speaker 1: client side evaluation. That's usually the signal that something in
Speaker 1: your query isn't translatable. The important thing to understand is
Speaker 1: that EF isn't running your code when you write it,
Speaker 1: it's diagramming it into this object tree, and if a
Speaker 1: part of that tree doesn't correspond to a known SEQL pattern,
Speaker 1: EF either stops or decides to push that part of
Speaker 1: the work into memory, which can be costly. Performance issues
Speaker 1: often show up here. Queries that seem harmless in C
Speaker 1: sides suddenly to thousands of rows being pulled client side
Speaker 1: because EF couldn't translate one small piece. And that's why
Speaker 1: expression trees matter to developers working with EF. They aren't
Speaker 1: just an internal detail, they are the roadmap EF uses
Speaker 1: before SQL even enters the picture. Every link query is
Speaker 1: first turned into this structural plan that EF studies carefully.
Speaker 1: Whether a query succeeds, fails, or slows down often depends
Speaker 1: on what that plan looks like. But there's still one
Speaker 1: more step in the process. Once EF has that expression tree,
Speaker 1: it can't just ship it off to the database. It
Speaker 1: needs a gatekeeper. Something has to decide whether each part
Speaker 1: of the tree is SQL legal or something that should
Speaker 1: never leave C culls. And that's where the next stage
Speaker 1: come in. The gatekeeper. EF cores query provider. Not every
Speaker 1: query you write in CS is destined to become SQL.
Speaker 1: There's a checkpoint in the middle of the pipeline, and
Speaker 1: its role is to decide what moves forward and what
Speaker 1: gets blocked. This checkpoint is implemented by ef core's query
Speaker 1: provider component, which evaluates whether the expression tree's nodes can
Speaker 1: be mapped to SQL or need to be handled in memory.
Speaker 1: You can picture the provider like a bouncer at a club.
Speaker 1: Everyone can show up in line, but only the queries
Speaker 1: dressed in SQL compatible patterns actually get inside. The rest
Speaker 1: either get turned away or get redirected for client side handling.
Speaker 1: It's not about being picky or arbitrary. The provider is
Speaker 1: enforcing the limits of translation link Q can represent far
Speaker 1: more than relational databases will ever understand. Ef core has
Speaker 1: to walk the expression tree and ask of each node,
Speaker 1: is this something SQL can handle or is it something
Speaker 1: net alone can execute. That call gets made early before
Speaker 1: CECL generation starts, which is why you sometimes see runtime
Speaker 1: errors upfront instead of confusing results later. For the developer,
Speaker 1: the surprise often comes from uneven support. Many constructs map cleanly,
Speaker 1: where select, order buy usually translate with no issue. Others
Speaker 1: are more complicated. For example, group by can be more
Speaker 1: difficult to translate, and depending on the provider and the scenario,
Speaker 1: it may either fail outright or produce sequel that isn't
Speaker 1: very efficient. Developers see this often enough that it's a
Speaker 1: known caution point, though the exact behavior depends on the
Speaker 1: provider's translation rules. The key thing the provider is doing
Speaker 1: here is pattern matching. It isn't inventing school on the
Speaker 1: fly in some magical way. Instead, it compares the expression
Speaker 1: tree against the library of translation patterns. It understands recognized
Speaker 1: shapes in the tree map to escual templates. Unrecognized ones
Speaker 1: either get deferred to client side execution or rejected, and
Speaker 1: that's why some complex queries work fine, while others lead
Speaker 1: to messages about unsupported translation. The decision is deterministic. It's
Speaker 1: all about whether a given pattern has a known valid
Speaker 1: sequel output, and this is also the stage where client
Speaker 1: side evaluation shows up. If a part of the query
Speaker 1: can't be turned into SQL YFCRE may still run it
Speaker 1: in memory after fetching the data At first glance. That
Speaker 1: seems practical. Sequel gives you the data net finishes the job,
Speaker 1: but the cost can be huge if the database hands
Speaker 1: over thousands or even millions of rows just so net
Speaker 1: can filter them. Afterward performance collapses. Something that looked innocent
Speaker 1: in a local test database can stall badly in production
Speaker 1: when the data volume grows. Developers often underestimate the shift.
Speaker 1: Think of a query that seems perfectly fine while developing
Speaker 1: against a data set of a few hundred rows. In production,
Speaker 1: the same query retrieves tens of thousands of records and
Speaker 1: runs a slow operation on the application server. That's when
Speaker 1: users start complaining that everything feels stuck. The provider's guardrails
Speaker 1: matter here, and in many cases it's safer to get
Speaker 1: an error than to let EF try to do something inefficient.
Speaker 1: For anyone building with EF, the practical takeaway is simple.
Speaker 1: Always test queries against real or representative data, and pay
Speaker 1: attention to whether performance suddenly knows dives in production. If
Speaker 1: it feels fast locally but drags underload, that's often as
Speaker 1: sign the provider has pushed part of your logic to
Speaker 1: client side. Evaluation is not automatically wrong, but it is
Speaker 1: a signal you need to pay closer attention. So while
Speaker 1: the provider is the gatekeeper, it isn't just standing guard.
Speaker 1: It's protecting both correctness and performance by filtering what can
Speaker 1: be te translated into SQL and controlling when to fall
Speaker 1: back to client side execution. It keeps your pipeline predictable.
Speaker 1: At the same time, it's under constant pressure to make
Speaker 1: these decisions quickly without rewriting your query structure from scratch
Speaker 1: every time. And that's where another piece of EF core's
Speaker 1: design becomes essential. A system to remember and reuse decisions
Speaker 1: rather than starting from zero on every request. Catching EF's
Speaker 1: secret performance weapon, here's where performance stops being theoretical. Entity
Speaker 1: Framework Core relies on caching as one of its biggest
Speaker 1: performance tools, and without it, query translation would be painfully inefficient.
Speaker 1: Every link query starts its life as an expression tree
Speaker 1: and has to be analyzed, validated, and prepared for sickle translation.
Speaker 1: Network isn't free if EF had to repeat it from
Speaker 1: scratch on every execution. Even simple queries would bog down
Speaker 1: once repeated frequently. To picture what that would mean in practice,
Speaker 1: think about running the same query thousands of times per
Speaker 1: second in a production app. Without caching. EF core would
Speaker 1: grind through full passing and translation on each call. The
Speaker 1: database wouldn't necessarily be the problem. Your CPU would spike
Speaker 1: just from EF redoing the prep work. This is why
Speaker 1: cachhing isn't an optional optimization, it's the foundation that makes
Speaker 1: ef core workable at real world scale. So how does
Speaker 1: it actually help? Efcre uses caching to recognize when a
Speaker 1: query shape it has already processed shows up again. Instead
Speaker 1: of reanalyzing the expression tree node by node, EF can
Speaker 1: reuse the earlier work, and that means when you filter
Speaker 1: by something like customer eyed, the first run takes longer
Speaker 1: while EF figures out how to map that filter into SQL.
Speaker 1: After that, subsequent executions with different parameter values are fast
Speaker 1: because the heavy lifting has already been stored in short
Speaker 1: first pass builds. The plan later passes reuse it now.
Speaker 1: The details of exactly how this cache is structured vary
Speaker 1: by ef core version and provider, but the general principle
Speaker 1: is consistent. The cash keeps track of repeated query shapes.
Speaker 1: When the model changes, say you add a property to
Speaker 1: an entity, the cased items are no longer valid and
Speaker 1: EF clears them. This prevents mismatched SQL from ever being reused.
Speaker 1: The implementation specifics, such as multiple caching layers or eviction
Speaker 1: rules are tied to version and configuration details and should
Speaker 1: be checked in official ef core documentation From a developer's perspective,
Speaker 1: the result is straightforward. Queries run noticeably faster after the
Speaker 1: first execution. That's caching at work. The benefit is easy
Speaker 1: to underestimate because the speed increase feels invisible until you
Speaker 1: turn cashing off or hit a pattern that doesn't reuse
Speaker 1: as efficiently. Once you realize what it's doing, you start
Speaker 1: to see why EF can stay response if even under
Speaker 1: heavy load. But cashing is not a free ride. Every
Speaker 1: cash entry takes memory, and applications with a high number
Speaker 1: of unique query shapes can see memory usage climb. If
Speaker 1: you rely heavily on dynamically composed queries. String building predicates
Speaker 1: run time generated projections or code that produces slightly different
Speaker 1: shapes every call, you'll generate many cash entries that never
Speaker 1: get reused. That's when the cash becomes a liability instead
Speaker 1: of an asset. Developers should keep an eye out for
Speaker 1: that pattern. Fewer, more consistent query shapes make the most
Speaker 1: of caching and avoid wasting memory. The trick for teams
Speaker 1: is recognizing that cased queries are both a performance advantage
Speaker 1: and a potential memory cost. You want to take advantage
Speaker 1: of caching on repetitive work queries you know will run
Speaker 1: thousands of times, but be aware of how your application
Speaker 1: builds queries. If you're generating too many unique ones, the
Speaker 1: cash has to hold onto shapes that are unlikely to
Speaker 1: be seen again. That can add unexpected weight to your system,
Speaker 1: especially at scale. In practice, the best advice is to
Speaker 1: let ef core handle caching automatically, but to be intentional
Speaker 1: about how you write queries. If you notice memory pressure
Speaker 1: in your application, while databaseload looks normal, consider whether the
Speaker 1: issue might be related to lots of cached query shapes.
Speaker 1: It's not the first place developers look, but it's often
Speaker 1: a silent contributor. Optimizing query patterns can be as important
Speaker 1: as optimizing the database itself. Cashing often explains why ef
Speaker 1: queries feel fast after that initial delay. It's doing the
Speaker 1: same job once, then skipping overhead on repeats. Simple but
Speaker 1: powerful still even when query execution feels smooth. Another source
Speaker 1: of subtle bugs lurks just around the corner handling null values.
Speaker 1: That's where ef core has to bridge two very different
Speaker 1: definitions of nothing, and it's a problem developers run into
Speaker 1: early time null semantics when nothing means different things. In
Speaker 1: most everyday coding developers can treat null as a simple concept,
Speaker 1: but the reality is more complicated once ef core sits
Speaker 1: between c BOSS and a SQL database. This is where
Speaker 1: the issue of null semantics takes center stage. The rules
Speaker 1: you think you're applying in NET don't always mean the
Speaker 1: same thing when the database evaluates them. In CR, null
Speaker 1: is straightforward a missing object reference, an unassigned string, a
Speaker 1: property that hasn't been set all amount to the same thing.
Speaker 1: But CQL operates differently. It doesn't use null in a
Speaker 1: way that lines up directly with NT. SQL treats it
Speaker 1: more like an unknown value, which affects how comparisons behave.
Speaker 1: For instance, in SQL, writing column in null will not
Speaker 1: behave like a true false test. Instead, it produces special
Speaker 1: handling that requires is null checks. This is a critical
Speaker 1: distinction developers need to keep in mind. A quick example
Speaker 1: makes the difference clear. Suppose you write for query customers
Speaker 1: where c Klow's c name exus null. Run that query
Speaker 1: in memory against the list of customer objects, and you'll
Speaker 1: reliably get back those whose name is actually null. Translate
Speaker 1: that same logic into SQL without adjustments, and you'd expect
Speaker 1: to see where no name ix null in practice, that
Speaker 1: would not return any rows at all. The correct circle
Speaker 1: form would be where name is null. Being mindful of
Speaker 1: the difference matters. As a developer. It's a good habit
Speaker 1: to check the circle output when your link you depends
Speaker 1: on null comparisons, especially to avoid surprises when moving to
Speaker 1: production data. This mismatch is at the root of why
Speaker 1: null queries sometimes behave so strangely in ef core. If
Speaker 1: left uncorrected, something that seems predictable in cpars could silently
Speaker 1: yield no results in the database. Occasionally, it might even
Speaker 1: give results that look fine in small tests but fail
Speaker 1: in real scenarios where nulls appear more often, that's an
Speaker 1: easy way for subtle bugs to sneak in without warning.
Speaker 1: To reduce this risk, ef core doesn't simply pass your
Speaker 1: null comparisons through. Instead, it applies rules to keep net
Speaker 1: and ccle behavior aligned. For equality checks, ef will usually
Speaker 1: adjust them into is null or is not null conditions.
Speaker 1: For more involved predicates, the pipeline often performs compensating transformations
Speaker 1: so database results stay in sync with what a NEET
Speaker 1: runtime logic would have done. The exact internals of these
Speaker 1: adjustments depend on vers and provider, but The guiding principle
Speaker 1: is consistent preserve developer expectations by normalizing null logic. However,
Speaker 1: this alignment comes at a cost. Those compensating transformations can
Speaker 1: make SEQL queries longer and more complex than what it
Speaker 1: seemed you wrote. EF is prioritizing correctness over simplicity, sometimes
Speaker 1: at the expense of efficiency. That's why you may occasionally
Speaker 1: see generated sequel with extra conditions that don't match your
Speaker 1: clean link statement. It's EF quietly ensuring you don't wake
Speaker 1: up to inconsistent results later. The complexity of the generated
Speaker 1: query is often the visible side effect of keeping null
Speaker 1: semantics safe across two systems with conflicting definitions. What matters
Speaker 1: most for developers is recognizing the potential risk in null handling.
Speaker 1: If a query appears odd, slow, or overly complex, null
Speaker 1: checks are a good place to start troubleshooting. A short
Speaker 1: but practical takeaway is this, if a query involving nulls
Speaker 1: behaves oddly, check for translation differences or hidden rewrites. These
Speaker 1: are not mistakes so much as protective guardrails ef core
Speaker 1: has built in. The Real danger is assuming high harmless
Speaker 1: null checks behave the same in both environments. They don't,
Speaker 1: and that can surface as bugs that only appear with
Speaker 1: production data not in a tidy test set. For example,
Speaker 1: you might think a filter excludes nulls until you notice
Speaker 1: certain records mysteriously missing. That kind of silent mismatch can
Speaker 1: be one of the hardest issues to track down unless
Speaker 1: you've validated the generated sequel against real data volumes and patterns.
Speaker 1: So while null semantics are a headache, they also represent
Speaker 1: one of ef core's most important interventions. By compensating for
Speaker 1: the mismatch, EF helps smooth over a gap that could
Speaker 1: otherwise cause unpredictable failures. Developers may not like the extra
Speaker 1: circle that shows up in the process, but without it,
Speaker 1: the results would be unreliable. Having dealt with nulls, EF
Speaker 1: is now carrying a query that's been passed, filtered through
Speaker 1: the provider, cashed, and adjusted to keep logic consistent. The
Speaker 1: final question is what happens next. How does this prepared
Speaker 1: query become a circle command that the database can actually execute,
Speaker 1: and how is the raw data turned back into usable
Speaker 1: and net objects For ess seqle generation and materialization, the
Speaker 1: last state of the pipeline is sequel generation and materialization,
Speaker 1: the point where all that preparation either pays off or
Speaker 1: falls apart. Everything up to now has been about shaping intent,
Speaker 1: validating patterns, and protecting consistency. But queries only become useful
Speaker 1: when efcor can turn that intent into a circle command
Speaker 1: your database understands, and then reshape the flat results into
Speaker 1: rich objects. Your code can actually work with two moving
Speaker 1: parts to the bulk of this work, the seqle generator
Speaker 1: and the materializer. They solve opposite problems but depend on
Speaker 1: each other. SEQLE generation is provider aware. Provider components influence
Speaker 1: how queries are expressed for a given database dialect. Materialization
Speaker 1: then takes the rows that come back and builds entities
Speaker 1: and projections into rsionnet. Neither side on its own is enough.
Speaker 1: SECLE generation ensures the database can run the query. Materialization
Speaker 1: ensures the results make sense for your application. That back
Speaker 1: and forth is why this stage feels like translation in
Speaker 1: two directions. A link filter that looked harmless in C
Speaker 1: parts needs to be written as valid sequel for postgress,
Speaker 1: equal sequel server, or whichever provider you using. When the
Speaker 1: database replies, ef receives nothing more than rows and columns,
Speaker 1: which it cannot simply hand to you without context. Your
Speaker 1: expectation is that you'll receive entities with navigation properties, wired up,
Speaker 1: typed values in the right places and relationships intact. Bridging
Speaker 1: that gap is what these steps are designed to do.
Speaker 1: Think about it with a simple example. If you've written
Speaker 1: a query that includes related entities, say an order with
Speaker 1: its order lines, you don't want to see half a
Speaker 1: dozen partial rows and stitch them together Manually. You expect
Speaker 1: to see an order object that contains a populated order
Speaker 1: lines collection. That's materialization in action. EF reconstructs a full
Speaker 1: object graph from sets of rows, and here's a practical pointer.
Speaker 1: If you're noticing duplicate tracked objects or missing navigation values,
Speaker 1: it often comes down to how those joints were shaped
Speaker 1: and how EF materialized the results. Sickle generation itself highlights
Speaker 1: EF's dependency on providers. The framework doesn't attempt to hard
Speaker 1: code every syntax detail. Instead, providers supply the logic for
Speaker 1: their database. That means the same ali ink query might
Speaker 1: render slightly different sequels in different environments. Brackets might appear
Speaker 1: on CQL server, quoted identifiers on postgresscool, different type co
Speaker 1: versions elsewhere. These variations matter because they determine whether the
Speaker 1: query is actually valid for the target database. This principle
Speaker 1: is worth confirming against the EF core docs for the
Speaker 1: specific version and provider you're using. Since capabilities evolve on
Speaker 1: the materialization side, EF has to handle more than just
Speaker 1: simple mappings. It needs to line up database column types
Speaker 1: with net types, enforce conversion when needed, and fix up
Speaker 1: foreign keys so relationships turn into real object references. Projections.
Speaker 1: Add another twist, A query that asks for a custom
Speaker 1: dto or an anonymous type must be assembled directly from
Speaker 1: the results set without ever creating a full entity. That
Speaker 1: flexibility is where developers feel EF adapting to their needs,
Speaker 1: but it adds real complexity to the engine underneath. There
Speaker 1: are also cases where the materializer tracks properties you didn't
Speaker 1: explicitly define. Features like shadow properties or lazy loading hooks
Speaker 1: fit here, but these vary by EF core, version and provider,
Speaker 1: so check the documentation of your target environment before relying
Speaker 1: on them. What matters most is that materialization manages to
Speaker 1: hide this entire process. Developers see a clean object model,
Speaker 1: while EF has spent considerable effort balancing performance with correctness relationships,
Speaker 1: give a good snapshot of the hidden work involved. Instead
Speaker 1: of handing you rows that reference each other by id,
Speaker 1: EF resolves those references into navigation properties. The tip here
Speaker 1: is simple. If navigation properties are empty or inconsistent, revisiting
Speaker 1: how you shape the query, especially with include or projection choices,
Speaker 1: can often resolve it. So in practice, secle, generation and
Speaker 1: materialization give EF its most visible impact. These are the
Speaker 1: stages that make the difference between a developer friendly experience
Speaker 1: and data plumbing that would otherwise consume hours of manual mapping.
Speaker 1: When you query with EF, you get back something that
Speaker 1: feels natural in net, not because seql gave you objects,
Speaker 1: but because EF rebuild them that way. This is why
Speaker 1: the process often feels like magic. Two different engines, one
Speaker 1: fluent in database dialects, the other fluent in and net
Speaker 1: objects hand off work seamlessly so you see only the
Speaker 1: finished result. But it isn't magic at all. It's a
Speaker 1: pipeline deliberately layered to keep performance, correctness, and usability in balance.
Speaker 1: And that careful layering is the real story behind Entity Framework,
Speaker 1: or what holds EF core together. Isn't magic, but a
Speaker 1: chain of deliberate steps. Expression trees, query providers, caching, null handling,
Speaker 1: and materialization, all shaping how your queries perform and behave.
Speaker 1: Knowing these moving pieces makes a difference, because a query
Speaker 1: that seems harmless in code can perform very differently. Under
Speaker 1: Load as practical next steps, keep three things in mind.
Speaker 1: Check generated skill for complex expressions, watch for signs of
Speaker 1: client side evaluation, and monitor how diverse your query shapes
Speaker 1: are to avoid unnecessary cash growth. Looking ahead, it's worth asking,
Speaker 1: as AI driven developer tools spread, could caching, null handling
Speaker 1: or ceqle translation be reimagined and what would it mean
Speaker 1: for frameworks like ef core. Share your own toughest query
Speaker 1: translation issues in the comments, and don't forget to like
Speaker 1: and subscribe. Understanding this pipeline is not just academic. It's
Speaker 1: essential for keeping your applications reliable and responsive.