alamb commented on a change in pull request #443: URL: https://github.com/apache/arrow-datafusion/pull/443#discussion_r644233517
########## File path: docs/specification/invariants.md ########## @@ -0,0 +1,327 @@ +<!--- + Licensed to the Apache Software Foundation (ASF) under one + or more contributor license agreements. See the NOTICE file + distributed with this work for additional information + regarding copyright ownership. The ASF licenses this file + to you under the Apache License, Version 2.0 (the + "License"); you may not use this file except in compliance + with the License. You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, + software distributed under the License is distributed on an + "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + KIND, either express or implied. See the License for the + specific language governing permissions and limitations + under the License. +--> + +# DataFusion's Invariants + +This document enumerates invariants of DataFusion's logical and physical planes +(functions, and nodes). Some of these invariants are currently not enforced. +This document assumes that the reader is familiar with some of the codebase, +including rust arrow's RecordBatch and Array. + +## Rational + +DataFusion's computational model is built on top of a dynamically typed arrow +object, Array, that offers the interface `Array::as_any` to downcast itself to +its statically typed versions (e.g. `Int32Array`). DataFusion uses +`Array::data_type` to perform the respective downcasting on its physical +operations. DataFusion uses a dynamic type system because the queries being +executed are not always known at compile time: they are only known during the +runtime (or query time) of programs built with DataFusion. This document is +built on top of this principle. + +In dynamically typed interfaces, it is up to developers to enforce type +invariances. This document declares some of these invariants, so that users +know what they can expect from a query in DataFusion, and DataFusion developers +know what they need to enforce at the coding level. + +## Notation + +* Field or physical field: the tuple name, `arrow::DataType` and nullability flag (a bool whether values can be null), represented in this document by `PF(name, type, nullable)` +* Logical field: Field with a relation name. Represented in this document by `LF(relation, name, type, nullable)` +* Projected plan: plan with projection as the root node. +* Logical schema: a vector of logical fields, used by logical plan. +* Physical schema: a vector of physical fields, used by both physical plan and Arrow record batch. + +### Logical + +#### Function + +An object that knows its valid incoming logical fields and how to derive its +output logical field from its arguments' logical fields. A functions' output +field is itself a function of its input fields: + +``` +logical_field(lf1: LF, lf2: LF, ...) -> LF +``` + +Examples: + +* `plus(a,b) -> LF(None, "{a} Plus {b}", d(a.type,b.type), a.nullable | b.nullable)` where d is the function mapping input types to output type (`get_supertype` in our current implementation). +* `length(a) -> LF(None, "length({a})", u32, a.nullable)` + +#### Plan + +A tree composed of other plans and functions (e.g. `Projection c1 + c2, c1 - c2 AS sum12; Scan c1 as u32, c2 as u64`) +that knows how to derive its schema. + +Certain plans have a frozen schema (e.g. Scan), while others derive their +schema from their child nodes. + +#### Column + +A type of logical node in a logical plan consists of field name and relation name. + +### Physical + +#### Function + +An object that knows how to derive its physical field from its arguments' +physical fields, and also how to actually perform the computation on data. A +functions' output physical field is a function of its input physical fields: + +``` +physical_field(PF1, PF2, ...) -> PF +``` + +Examples: + +* `plus(a,b) -> PF("{a} Plus {b}", d(a.type,b.type), a.nullable | b.nullable)` where d is a complex function (`get_supertype` in our current implementation) whose computation is for each element in the columns, sum the two entries together and return it in the same type as the smallest type of both columns. +* `length(&str) -> PF("length({a})", u32, a.nullable)` whose computation is "count number of bytes in the string". + +#### Plan + +A tree (e.g. `Projection c1 + c2, c1 - c2 AS sum12; Scan c1 as u32, c2 as u64`) +that knows how to derive its metadata and compute itself. + +Note how the physical plane does not know how to derive field names: field +names are solely a property of the logical plane, as they are not needed in the +physical plane. + +#### Column + +A type of physical node in a physical plan consists of a field name and unique index. + +### Data Sources' registry + +A map of source name/relation -> Schema plus associated properties necessary to read data from it (e.g. file path). + +### Functions' registry + +A map of function name -> logical + physical function. + +### Physical Planner + +A function that knows how to derive a physical plan from a logical plan: + +``` +plan(LogicalPlan) -> PhysicalPlan +``` + +### Logical Optimizer + +A function that accepts a logical plan and returns an (optimized) logical plan +which computes the same results, but in a more efficient manner: + +``` +optimize(LogicalPlan) -> LogicalPlan +``` + +### Physical Optimizer + +A function that accepts a physical plan and returns an (optimized) physical +plan which computes the same results, but may differ based on the actual +hardware or execution environment being run: + +``` +optimize(PhysicalPlan) -> PhysicalPlan +``` + +### Builder + +A function that knows how to build a new logical plan from an existing logical +plan and some extra parameters. + +``` +build(logical_plan, params...) -> logical_plan +``` + +## Invariants + +The following subsections describe invariants. Since functions' output schema +depends on its arguments' schema (e.g. min, plus), the resulting schema can +only be derived based on a known set of input schemas (TableProvider). +Likewise, schemas of functions depend on the specific registry of functions +registered (e.g. does `my_op` return u32 or u64?). Thus, in this section, the +wording "same schema" is understood to mean "same schema under a given registry +of data sources and functions". + +### (relation, name) tuples in logical fields and logical columns are unique + +Every logical field's (relation, name) tuple in a logical schema MUST be unique. +Every logical column's (relation, name) tuple in a logical plan MUST be unique. + +This invariant guarantees that `SELECT t1.id, t2.id FROM t1 JOIN t2...` +unambiguously selects the field `t1.id` and `t2.id` in a logical schema in the +logical plane. + +#### Responsibility + +It is the logical builder and optimizer's responsibility to guarantee this +invariant. + +#### Validation + +Builder and optimizer MUST error if this invariant is violated on any logical +node that creates a new schema (e.g. scan, projection, aggregation, join, etc.). + +### Physical schema is consistent with data + +The contents of every Array in every RecordBatch in every partition returned by +a physical plan MUST be consistent with recordBatch's schema, in that every +Array in the RecordBatch must be downcastable to its corresponding type +declared in the RecordBatch. + +#### Responsibility + +Physical functions MUST guarantee this invariant. This is particularly +important in aggregate functions, whose aggregating type may be different from +the intermediary types during calculations (e.g. sum(i32) -> i64). + +#### Validation + +Since the validation of this invariant is computationally expensive, execution +contexts CAN validate this invariant. It is acceptable for physical nodes to +`panic!` if their input does not satisfy this invariant. + +### Physical schema is consistent in physical functions + +The schema of every Array returned by a physical function MUST match the +DataType derived by the physical function itself. + +This ensures that when a physical function claims that it returns a type +(e.g. Int32), users can safely downcast its resulting Array to the +corresponding type (e.g. Int32Array), as well as to write data to formats that +have a schema with nullability flag (e.g. parquet). + +#### Responsibility + +It is the responsibility of the developer that writes a physical function to +guarantee this invariant. + +In particular: + +* The derived DataType matches the code it uses to build the array for every branch of valid input type combinations. +* The nullability flag matches how the values are built. + +#### Validation + +Since the validation of this invariant is computationally expensive, execution +contexts CAN validate this invariant. + +### The physical schema is invariant under planning + +The physical schema derived by a physical plan returned by the planner MUST be +equivalent to the physical schema derived by the logical plan passed to the +planner. Specifically: + +``` +plan(logical_plan).schema === logical_plan.physical_schema +``` + +Logical plan's physical schema is defined as logical schema with relation Review comment: I think it is good enough as written. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected]
