5 posts tagged with "query"

It is tempting to describe TeaQL as an ORM because TeaQL knows about entities, relations, repositories, and databases.

That description is incomplete.

TeaQL is a generated business API layer. Persistence is one part of the system, but the main value is the generated domain language that sits above persistence.

What an ORM Usually Optimizes​

Most ORM discussions focus on mapping:

• classes to tables;
• fields to columns;
• relations to joins;
• objects to rows;
• transactions to persistence sessions.

Those are real problems. TeaQL also needs to solve them. But large business systems have another repeated problem: the same business request is rebuilt again and again in controllers, repositories, DTOs, SQL, validators, and frontend response logic.

What TeaQL Optimizes​

TeaQL optimizes the business API surface.

It generates request APIs that can express:

• selection;
• nested relation loading;
• filters;
• list-existence queries;
• pagination;
• grouped statistics;
• relation aggregates;
• graph writes;
• validation hooks;
• runtime customization.

The generated API is meant to be read by backend engineers, domain engineers, reviewers, and AI coding tools.

Generated Business APIs​

An ORM might make it easy to load an Order.

TeaQL aims to make it clear how a complete order page is assembled:

Q.orders()
    .filterByMerchant(ctx.getMerchant())
    .selectCustomer(Q.customers().selectName())
    .selectLineItemList(Q.lineItems().selectSku().selectQuantity())
    .countLineItems()
    .orderByCreateTimeDescending()
    .page(1, 20)
    .executeForList(ctx);

The key is not whether this compiles to SQL. It does. The key is that the API names the business shape before the runtime turns it into storage operations.

Runtime Boundary​

TeaQL also treats runtime behavior as part of the model:

• user context;
• tenant and permission policy;
• cache behavior;
• distributed locks;
• logging and metrics;
• validation;
• event dispatch;
• database provider selection.

That is why TeaQL has a runtime layer instead of only a mapper layer.

Why This Helps AI​

AI tools should not need to guess SQL, join rules, table names, tenant filters, and response shapes from scattered code.

Generated APIs give AI tools a deterministic vocabulary:

• call this field selector;
• use this relation loader;
• compose this query fragment;
• execute through this runtime context;
• do not bypass the provider.

That is a different goal from a traditional ORM.

The Short Version​

TeaQL uses persistence mapping, but it is not defined by persistence mapping.

It is a generated business API platform that keeps domain intent visible while allowing the runtime provider to change underneath.

MyBatis is a productive and familiar tool for Java teams. It gives developers direct control over SQL and mapping. For many simple services, that is enough.

The pressure appears when a business page needs more than one table and more than one query shape.

Consider an order page.

The Order Page Shape​

A typical order page may need:

• the order list;
• customer name;
• order status;
• line item preview;
• line item count;
• status counts for tabs;
• pagination;
• merchant or tenant filtering;
• permission-aware visibility.

With mapper-oriented persistence, this often becomes several mapper methods, XML fragments, DTO assembly, and post-query stitching.

The MyBatis Shape​

A MyBatis implementation might involve:

• one SQL query for the order list;
• one query or join for customer fields;
• one query for line item previews;
• one query for counts;
• one query for grouped status statistics;
• result maps or manual DTO mapping;
• service code to assemble the final response.

That is explicit and controllable, but the business intent is spread across files.

The TeaQL Shape​

TeaQL tries to express the page as one generated business request:

User userOrderInfo = Q.users()
    .filterWithId(userId)
    .countOrder()
    .facetByOrderStatus("statusWithCount", Q.orderStatus().countOrders())
    .selectOrderList(
        Q.ordersWithId()
            .selectOrderId()
            .selectDate()
            .offset(0, 10)
            .selectLineItemList(
                Q.lineItemsWithId().selectImageURL().limit(3)
            )
            .countLineItems()
    )
    .execute(context);

The API is still explicit, but the explicitness is at the business level:

• user;
• orders;
• status counts;
• order list;
• line items;
• pagination;
• line item count.

What TeaQL Reduces​

TeaQL does not eliminate thinking. It reduces repetitive plumbing:

• mapper XML;
• duplicated DTO assembly;
• repeated relation-loading code;
• repeated count/statistics query fragments;
• stringly-typed field references;
• one-off page query services.

The generated model becomes the shared vocabulary.

Where MyBatis Still Wins​

MyBatis remains a good choice when:

• a query must be hand-tuned;
• SQL is the clearest expression of the business requirement;
• the domain model is small;
• teams need exact control over vendor-specific SQL;
• generated APIs would add weight without reducing repetition.

TeaQL and MyBatis do not need to be ideological opposites. TeaQL is valuable when the business model is large enough that generated APIs reduce repeated work.

The Review Difference​

In a TeaQL code review, the reviewer can ask:

• does this page select the correct relations?
• are the counts grouped correctly?
• is pagination applied at the right level?
• does the runtime context apply tenant and permission policy?

That is a higher-level review than checking XML fragments and DTO stitching.

The Short Version​

MyBatis gives direct SQL control. TeaQL gives generated business APIs.

For complex business pages, TeaQL keeps page intent closer to the domain model.

TeaQL is a data layer for applications where the domain model is the center of the system rather than a thin mapping over tables. The current Rust implementation carries over ideas from the Java TeaQL stack, but with a smaller scope: PostgreSQL, SQLite, a Rust-native query AST, generated typed APIs, and no web framework dependency.

The goal is direct: instead of writing most application data access as handmade repository methods or raw SQL fragments, a domain model can generate a Rust crate with entity types, relation metadata, query builders, checker hooks, behavior hooks, and graph-save entrypoints.

Application code then works with a high-level API:

let platforms = Q::platforms()
    .select_merchant_list_with(
        Q::merchants()
            .select_name()
            .which_names_contain("TeaQL"),
    )
    .execute_for_list(&ctx)
    .await?;

This is not meant to replace every way of using SQL from Rust. If a service is mostly carefully tuned SQL, direct sqlx is probably a better fit. If the preferred abstraction is an ORM with a large Rust ecosystem, Diesel or SeaORM will be more familiar. TeaQL is aimed at a different case: large domain models where repeated relation loading, graph persistence, validation, and statistics queries become their own layer of application logic.

Why Generate the API?​

The original pressure came from systems where the same entity model needed to support several kinds of behavior:

• ordinary list and detail queries;
• nested relation loading, including paths such as merchant.platform or platform.merchant_list;
• graph writes where a parent object and children are committed together;
• additive schema bootstrap for development and tests;
• checker and validation logic that can inspect and fix entities;
• simple and grouped statistics;
• JSON serialization and JSON-expression style search.

You can build all of that by hand, but the code tends to become repetitive in two places. First, relation names and field names are repeated across query methods, repository code, and validation code. Second, application developers end up switching between typed domain objects and untyped row maps. TeaQL tries to keep the generated surface typed while letting the runtime keep a generic query and graph model internally.

For example, a generated service crate exposes Q::platforms() and Q::merchants() rather than asking application code to construct SelectQuery::new("Platform") directly. Low-level query objects still exist, but they are not the normal application-level API.

Runtime Pieces​

The Rust workspace is split into small crates:

• teaql-core: values, records, entity descriptors, query AST, expressions, commands, and SmartList<T>;
• teaql-sql: SQL compilation and dialect-neutral compiled query types;
• teaql-runtime: UserContext, repository resolution, behavior hooks, checker hooks, graph writes, relation enhancement, events, and optional SQLx executors;
• teaql-macros: #[derive(TeaqlEntity)] for descriptors and typed record/entity mapping;
• teaql-provider-sqlx-postgres, teaql-provider-sqlx-sqlite, teaql-provider-sqlx-mysql, and teaql-provider-rusqlite: provider adapters and dialect-specific execution paths.

Generated crates sit above those runtime crates. A generated CRM or ERP service, for example, exports entities such as Platform and Merchant, a Q query facade, behavior skeletons, checker skeletons, repository registration, and runtime module assembly helpers.

Query Construction​

TeaQL has a generic query AST, but generated code provides a domain-specific facade. Instead of this in application code:

let query = SelectQuery::new("Merchant")
    .project("id")
    .project("name")
    .filter(Expr::contains("name", "tea"));

the generated API can expose:

let merchants = Q::merchants()
    .select_name()
    .which_names_contain("tea")
    .order_by_create_time_desc()
    .page(1, 20)
    .execute_for_list(&ctx)
    .await?;

Relation loading uses the same style:

let platforms = Q::platforms()
    .select_merchant_list_with(
        Q::merchants()
            .select_name()
            .select_platform(),
    )
    .execute_for_list(&ctx)
    .await?;

One implementation detail mattered here: when a child is attached to a parent relation list, the reverse object relation should be populated too. In the example above, each merchant in platform.merchant_list should have its platform relation set. Otherwise, the result is typed but not really a domain object graph.

Graph Writes​

TeaQL has a save_graph path for committing complex objects. In generated crates, application code can call a typed save helper:

merchant
    .update_name("TeaQL Merchant")
    .update_platform_id(1_u64)
    .save(&ctx)
    .await?;

Internally, the runtime turns that object into a graph plan. The plan classifies nodes by entity and operation: create, update, delete/remove, or reference. It then batches compatible work where possible and runs the graph write inside a transactional executor.

The interesting part is not only inserting children. Updating a graph means answering questions like:

• is this child new, already present, a reference, or explicitly removed?
• should missing children be soft-deleted or left alone?
• should a relation write attach a foreign key, or is it detached?
• if a reference points at a deleted row or the wrong version, should the graph write fail?

The Rust runtime currently supports nested create/update graph writes, reference-only nodes, explicit remove nodes, keep-missing relation metadata, duplicate child-id rejection, and transaction rollback for SQLite and PostgreSQL SQLx executors.

Schema Bootstrap​

For local development and generated-service tests, TeaQL can bootstrap a schema from entity descriptors:

ctx.ensure_sqlite_schema().await?;

The current scope is intentionally conservative. It creates missing tables and adds missing columns. It does not try to be a destructive migration tool: no column drops, no primary-key rebuilds, and no automatic type rewrites.

That line is important because generated domain models change frequently. The bootstrap path should be safe enough for local and CI use, not pretend to replace a real production migration process.

Checkers and Domain Validation​

A checker is not just a validator that rejects a row. It can inspect an object, add structured check results, and sometimes fix fields before persistence.

The generated Rust checker support lets application code write typed checker logic instead of manually reading from a Record:

impl MerchantCheckerLogic for MerchantNameChecker {
    fn check_and_fix_merchant(
        &self,
        _ctx: &UserContext,
        entity: &mut Merchant,
        status: CheckObjectStatus,
        location: &ObjectLocation,
        results: &mut CheckResults,
    ) {
        if status.is_create() {
            self.required_text(&entity.name(), "name", location, results);
        }

        self.min_string_length(&entity.name(), "name", 3, location, results);

        if entity.name() == "fix" {
            entity.update_name("fixed");
        }
    }
}

The runtime still stores the common checker interface at the record level, but the generated adapter maps records into typed entities before calling the checker. That keeps the public application code close to the domain model while preserving a generic runtime path.

Statistics​

TeaQL queries can carry aggregate projections and relation aggregate metadata. The current runtime supports simple aggregates, grouped aggregates, Decimal results for SQL aggregate output, relation count/statistic attachment, and database-column-to-entity-property mapping for relation aggregate keys.

The generated Q APIs can express both simple statistics and relation statistics. For example, a service can count child rows from a parent query without asking application code to hand-build the join every time.

This area is useful but still evolving. The runtime has working SQL and memory paths for the core cases, while broader Java parity still needs more work around memory subqueries and richer relation aggregate shapes.

Tradeoffs​

The most obvious tradeoff is generated code. TeaQL generates a lot of Rust. That cost shows up as compile time, larger diffs, and the need to keep templates disciplined. The benefit is that application code gets a stable, typed facade over a large domain model.

Another tradeoff is that the runtime is not purely compile-time checked. The generated APIs are typed, but the runtime still has a generic query AST, record model, and descriptor registry. That gives it flexibility for dynamic projections, aggregate rows, JSON-style search, and graph planning, but it means some mistakes are caught by generated crate tests rather than by Rust types alone.

The final tradeoff is scope. The Rust rewrite is not trying to clone every Java TeaQL feature or support every database. PostgreSQL and SQLite are enough for now. Web rendering, GraphQL integration, and broad database dialect support are outside the current Rust scope.

What Works Today​

The current Rust runtime and generated crate tests cover:

• SQLite schema bootstrap and additive column changes;
• PostgreSQL schema bootstrap with SQLx;
• CRUD, optimistic locking, soft delete, and recover;
• typed entity fetch into SmartList<T>;
• nested relation enhancement;
• complex object commit through graph writes;
• transaction rollback for graph writes;
• generated Q APIs against SQLite;
• typed checker adapters from generated crates;
• JSON serialization and JSON-expression search paths;
• simple aggregates, grouped aggregates, and relation aggregate statistics.

The public examples can be run with:

cargo run -p teaql-examples --bin sqlite_schema_crud
cargo run -p teaql-examples --bin sqlite_relations_graph

The first command shows schema bootstrap and CRUD against in-memory SQLite. The second saves an object graph and reloads nested relations.

What Is Not Done​

The biggest gaps are:

• more complete memory repository parity for relation enhancement and subquery execution;
• richer checker semantics, especially nested typed object locations and domain-specific labels;
• richer event payloads with old/new values and typed snapshots;
• more value types such as UUID and bytes;
• a decision on whether Rust needs a higher-level service layer above the repository/runtime APIs.

Those gaps are real. They are better kept visible than hidden behind a larger feature list.

Why This Shape Is Worth Exploring​

Most Rust database libraries are good at one of two layers: explicit SQL, or a database-centric ORM. TeaQL explores a third shape: generated domain APIs over a generic runtime that understands entity graphs, relation enhancement, validation, and statistics.

That shape will not fit every codebase. It is most useful when the model is large enough that the generated API becomes an asset, and when the team wants the same domain semantics to appear in queries, graph writes, checkers, and schema bootstrap.

TeaQL generates query APIs such as Q::platforms() and Q::merchants(). That immediately raises a fair question: why not just write SQL?

The short answer is that TeaQL is not trying to replace handwritten SQL everywhere. Generated query APIs are useful when the repeated work is not the SQL text itself, but the domain semantics around the SQL: field names, relation names, relation loading, statistics, validation, JSON search, and graph-shaped results.

The Handwritten SQL Case​

There are many cases where handwritten SQL is the right tool:

• a query is performance-critical and carefully tuned;
• the shape is reporting-specific rather than domain-object-specific;
• the team wants exact control over joins, hints, indexes, and execution plans;
• the service is small enough that generated APIs would add more weight than value.

TeaQL should not get in the way of those cases. The Rust runtime keeps a generic query AST and SQL compiler, but the design does not require every query to be expressed through generated methods.

If explicit SQL is the clearest abstraction, use explicit SQL.

The Generated API Case​

Generated APIs become useful when a large domain model produces many ordinary queries with the same repeated structure:

let merchants = Q::merchants()
    .select_name()
    .select_platform_with(Q::platforms().select_name())
    .which_names_contain("tea")
    .order_by_create_time_desc()
    .page(1, 20)
    .execute_for_list(&ctx)
    .await?;

This is not shorter than SQL in every case. The value is that the query is tied to the generated domain model:

• fields are exposed as methods;
• object relations are selected by relation name, not storage column name;
• readable filters follow the model vocabulary;
• subqueries can be constructed from the target entity's own generated API;
• execution returns typed entities or typed SmartList<T> collections.

That matters when the model is large and the same entity relationships appear across many services, pages, and business workflows.

Relation Semantics​

A generated API can encode relation semantics that plain SQL does not name.

For example:

let platforms = Q::platforms()
    .select_merchant_list_with(
        Q::merchants()
            .select_name()
            .select_platform(),
    )
    .execute_for_list(&ctx)
    .await?;

The application is not just asking for a join. It is asking for a platform list where each platform contains a merchant_list, and each merchant can carry its reverse platform relation when selected. That object shape is a domain result, not only a SQL result set.

The runtime still compiles to SQL and fetches records. The generated API gives the caller a domain-oriented way to request the shape.

Subqueries Without Stringly-Typed Entity Names​

TeaQL still has SelectQuery internally. Application code can use it directly, but generated crates should prefer the entity-specific Q entrypoints:

let platforms = Q::platforms()
    .select_merchant_list_with(
        Q::merchants()
            .which_names_are("TeaQL Merchant"),
    )
    .execute_for_list(&ctx)
    .await?;

This keeps the application out of string-based construction such as SelectQuery::new("Merchant") for ordinary code. The generic query type remains available for runtime and escape-hatch scenarios.

Statistics​

Large business systems rarely stop at list queries. They also need counts, sums, grouped aggregates, relation counts, and derived summary rows.

A generated API can give those operations domain names:

let platforms = Q::platforms()
    .select_name()
    .count_merchant_list_as("merchant_count")
    .execute_for_list(&ctx)
    .await?;

The runtime can attach relation aggregate results back to parent rows, while the generated request builder keeps the caller focused on domain concepts.

This does not remove the need for handwritten reporting SQL. It gives ordinary domain statistics a consistent path.

Why Keep a Generic Runtime?​

If generated APIs are useful, why not make the whole runtime typed?

Because TeaQL still needs dynamic behavior:

• generated service crates vary by domain model;
• aggregate rows can contain dynamic projection fields;
• JSON-expression search and raw SQL escape hatches need flexible query metadata;
• graph planning operates across entity types;
• relation enhancement starts with records and then maps into typed entities.

The generated layer is typed for application ergonomics. The runtime layer is generic so it can plan, compile, enhance, validate, and execute across many generated models.

Where Generated APIs Hurt​

Generated code is not free:

• it increases compile time;
• it creates large diffs when templates change;
• it can hide SQL details if developers treat it as magic;
• it needs strong generated-crate tests to catch template regressions.

TeaQL tries to offset that with explicit generated code, local runtime crates, and generated service tests that run real SQLite scenarios. The code is not hidden behind a server or reflection system; it is Rust source that can be inspected.

Still, generated APIs should be used where they buy something. A small service with ten queries may be better served by direct sqlx.

A Practical Boundary​

The boundary I use is this:

• if the query expresses a domain object shape, relation graph, validation path, or repeated business filter, prefer the generated Q API;
• if the query expresses a one-off report, hand-tuned performance path, or database-specific operation, prefer handwritten SQL;
• if generated code starts hiding a critical execution detail, drop down a level.

TeaQL's design only works if the lower level remains available.

The Real Goal​

The goal is not to win an argument against SQL. SQL remains the execution language and often the best authoring language.

The goal is to avoid making every application workflow manually rediscover the same domain relationships. In a large model, the generated API becomes a shared vocabulary: Q::platforms(), select_merchant_list_with(...), have_platform(), count_merchant_list_as(...), and so on.

That vocabulary is where TeaQL is useful. Not because SQL is bad, but because large domain models need more than SQL strings to stay coherent.

The Rust runtime now supports typed graph mutations and a fluent query DSL.

Typed Entity Graph Saves​

let mut graph = EntityGraph::new();
graph.add(User::new().name("Alice"));
graph.add(Order::new().user_id("Alice.id"));

db.save_graph(&graph).await?;

The runtime validates foreign keys, plans insertion order, and executes within a transaction.

SQL Id Space Generator​

let id_gen = SqlIdSpace::new("user_id_seq", 1000);
let batch = id_gen.next_batch(10).await?;

Reserves id ranges in bulk. Reduces database round-trips during bulk inserts.

Decimal for Aggregates​

let total: Decimal = db.query(Order::total())
    .filter(Order::status().eq("paid"))
    .sum()
    .await?;

rust_decimal replaces f64 for all aggregate results. Eliminates floating-point drift in financial calculations.

Query DSL​

let users = db.query(User::class())
    .filter(User::age().gte(18))
    .order_by(User::created_at().desc())
    .limit(20)
    .list()
    .await?;

Methods chain naturally. The macro expands field references into type-safe column identifiers at compile time.

Checker Events​

Graph mutations emit checker events:

• Pre-save validation
• Post-save side effects
• Rollback on failure

This hooks into the transactional planner for complex business rules.