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Package org.apache.drill.exec.vector.accessor

Provides a light-weight, simplified set of column readers and writers that can be plugged into a variety of row-level readers and writers.

See: Description

Package org.apache.drill.exec.vector.accessor Description

Provides a light-weight, simplified set of column readers and writers that can be plugged into a variety of row-level readers and writers. The classes and interfaces here form a framework for accessing rows and columns, but do not provide the code to build accessors for a given row batch. This code is meant to be generic, but the first (and, thus far, only) use is with the test framework for the java-exec project. That one implementation is specific to unit tests, but the accessor framework could easily be used for other purposes as well.

Vector Overflow Handling

The writers provide integrated support for detecting and handling vector overflow. Overflow occurs when a value exceeds some maximum, such as the 16MB block size in Netty. Overflow handling consists of replacing the "full" vector with a new, empty vector as part of a new batch. Overflow handing code must copy partially written values from the "overflow" row to the new vectors. The classes here do not provide overflow handling, rather they provide the framework on top of which overflow handling can be built by a higher level of abstraction.

JSON-Like Model

The object reader and writer provide a generic, JSON-like interface to allow any valid combination of readers or writers (generically accessors):

 row : tuple
 tuple : (name column) *
 column : scalar obj | array obj | tuple obj
 scalar obj : scalar accessor
 array obj : array accessor
 array accessor : element accessor
 tuple obj : tuple

As seen above, the accessor tree starts with a tuple (a row in the form of a class provided by the consumer.) Each column in the tuple is represented by an object accesor. That object accessor contains a scalar, tuple or array accessor. This models Drill's JSON structure: a row can have a list of lists of tuples that contains lists of ints, say.

Comparison with Previous Vector Readers and Writers

Drill provides a set of vector readers and writers. Compared to those, this set: The key difference is that this set is designed for both developer ease-of-use and performance. Developer eas-of-use is a primary requirement for unit tests. Performance is critical for production code. The other set is designed to be used in machine-generated or write-once code and so can be much more complex.

Overview of the Code Structure

ScalarReader and ColumnWriter are the core abstractions: they provide simplified access to the myriad of Drill column types via a simplified, uniform API. TupleReader and TupleWriter provide a simplified API to rows or maps (both of which are tuples in Drill.) AccessorUtilities provides a number of data conversion tools.
ObjectWriter, ObjectReader
Drill follows a JSON data model. A row is a tuple (AKA structure). Each column is a scalar, a map (AKA tuple, structure) or an array (AKA a repeated value.)
TupleWriter, TupleReader
In relational terms, a tuple is an ordered collection of values, where the meaning of the order is provided by a schema (usually a name/type pair.) It turns out that Drill rows and maps are both tuples. The tuple classes provide the means to work with a tuple: get the schema, get a column by name or by position. Note that Drill code normally references columns by name. But, doing so is slower than access by position (index). To provide efficient code, the tuple classes assume that the implementation imposes a column ordering which can be exposed via the indexes.
ScalarWriter, ScalarReader
A uniform interface for the scalar types: Nullable (Drill optional) and non-nullable (Drill required) fields use the same interface. Arrays (Drill repeated) are special. To handle the array aspect, even array fields use the same interface, but the getArray method returns another layer of accessor (writer or reader) specific for arrays.

Both the column reader and writer use a reduced set of data types to access values. Drill provides about 38 different types, but they can be mapped to a smaller set for programmatic access. For example, the signed byte, short, int; and the unsigned 8-bit, and 16-bit values can all be mapped to ints for get/set. The result is a much simpler set of get/set methods compared to the underlying set of vector types.

ArrayWriter, ArrayReader
The interface for the array accessors as described above. Of particular note is the difference in the form of the methods. The writer has only a setInt() method, no index. The methods assume write-only, write-once semantics: each set adds a new value. The reader, by contrast has a getInt(int index) method: read access is random.
Because of the form of the array writer, both the array writer and column writer have the same method signatures. To avoid repeating these methods, they are factored out into the common ScalarWriter interface.
ColumnAccessors (templates)
The Freemarker-based template used to generate the actual accessor implementations.
ColumnAccessors (accessors)
The generated accessors: one for each combination of write/read, data (minor) type and cardinality (data model).
ColumnReaderIndex, ColumnWriterIndex
This nested class binds the accessor to the current row position for the entire record batch. That is, you don't ask for the value of column a for row 5, then the value of column b for row 5, etc. as with the "raw" vectors. Instead, the implementation sets the row position (with, say an iterator.) Then, all columns implicitly return values for the current row.

Different implementations of the row index handle the case of no selection vector, a selection vector 2, or a selection vector 4.

The readers can work with single batches or "hyper" batches. A hyper batch occurs in operators such as sort where an operator references a collection of batches as if they were one huge batch. In this case, each column consists of a "stack" of vectors. The vector accessor picks out one vector from the stack for each row. Vector accessors are used only for hyper batches; single batches work directly with the corresponding vector.

You can think of the (row index + vector accessor, column index) as forming a coordinate pair. The row index provides the y index (vertical position along the rows.) The vector accessor maps the row position to a vector when needed. The column index picks out the x coordinate (horizontal position along the columns.)

Column Writer Optimizations

The writer classes here started as a simple abstraction on top of the existing vector mutators. The classes were then recruited for use in a new writer abstraction for Drill's record readers. At that point, performance became critical. The key to performance is to bypass the vector and the mutator and instead work with the Netty direct memory functions. This seems a risky approach until we realize that the writers form a very clear interface: the same interface supported the original mutator-based implementation and the revised Netty-based implementation. The benefit, however, is stark; the direct-to-Netty version is up to 4x faster (for repeated types).

Tuple Model

Drill has the idea of row and of a map. (A Drill map is much like a "struct": every instance of the "map" must have the same columns.) Both are instances of the relational concept of a "tuple." In relational theory, a tuple is a collection of values in which each value has a name and a position. The name is for the user, the position (index) allows efficient implementation.

Drill is unusual among query and DB engines in that it does not normally use indexes. The reason is easy to understand. Suppose two files contain columns a and b. File 1, read by minor fragment 0, contains the columns in the order (a, b). But, file 2, read by minor fragment 1, contains the columns in the order (b, a). Drill considers this the same schema. Since column order can vary, Drill has avoided depending on column order. (But, only partly; many bugs have cropped up because some parts of the code do require common ordering.)

Here we observe that column order varies only across fragments. We have control of the column order within our own fragment. (We can coerce varying order into a desired order. If the above two files are read by the same scan operator, then the first file sets the order at (a, b), and the second files (b, a) order can be coerced into the (a, b) order.

Given this insight, the readers and writers here promote position to a first-class concept. Code can access columns by name (for convenience, especially in testing) or by position (for efficiency.)

Further, it is often convenient to fetch a column accessor (reader or writer) once, then cache it. The design here ensures that such caching works well. The goal is that, eventually, operators will code-generate references to cached readers and writers instead of generating code that works directly with the vectors.

Lists and Unions

Drill provides a List and a Union type. These types are incomplete, buggy and ill-supported by Drill's operators. However, they are key to Drill's JSON-powered, schema-free marketing message. Thus, we must support them in the reader/writer level even if they are broken and under-used elsewhere in Drill. (If we did not support them, then the JSON readers could not use the new model, and we'd have to support both the old and new versions, which would create a bigger mess than we started with.)

Drill's other types have a more-or-less simple mapping to the relational model, allowing simple reader and writer interfaces. But, the Union and List types are not a good fit and cause a very large amount of complexity in the reader and writer models.

A Union is just that: it is a container for a variety of typed vectors. It is like a "union" in C: it has members for each type, but only one type is in use at any one time. However, unlike C, the implementation is more like a C "struct" every vector takes space or every row, even if no value is stored in that row. That is, a Drill union is as if a naive C programmer used a "struct" when s/he should have used a union.

Unions are designed to evolve dynamically as data is read. Suppose we read the following JSON:

 {a: 10} {a: "foo"} {a: null} {a: 12.34}
Here, we discover the need for an Int type, then a Varchar, then mark a value as null and finally a Float. The union adds the desired types as we request them. The writer mimics this behavior, using a listener to do the needed vector work.

Further, a union can be null. It carries a types vector that indicates the type of each row. A zero-value indicates that the union as a whole is null. In this case, null means no value, is is not, say, a null Int or null Varchar: it is simply null (as in JSON). Since at most one vector within the union carries a value, the element vectors must also be nullable. This means that a union has two null bits: one or the union, the other for the selected type. It is not clear what Drill semantics are supposed to be. Here the writers assume that either the whole union is null, or that exactly one member is non-null. Readers are more paranoid: they assume each member is null if either the union is null or the member itself is null. (Yes, a bit of a mess...)

The current union vector format is highly inefficient. If the union concept is needed, then it should be redesigned, perhaps as a variable-width vector in which each entry consists of a type/value pair. (For variable-width values such as strings, the implementation would be a triple of (type, length, value). The API here is designed to abstract away the implementation and should work equally well for the current "union" implementation and the possible "variant" implementation. As a result, when changing the API, avoid introducing methods that assume an implementation.

Lists add another layer of complexity. A list is, logically, a repeated union. But, for whatever historical reasons, a List can be other things as well. First, it can have no type at all: a list of nothing. This likely has no meaning, but the semantics of the List vector allow it. Second, the List can be an array of a single type in which each entry can be null. (Normal Repeated types can have an empty array for a row, but cannot have a null entry. Lists can have either an empty array or a null array in order to model the JSON null and [] cases.)

When a List has a single type, it stores the backing vector directly within the List. But, a list can also be a list of unions. In this case, the List stores a union vector as its backing vector. Here, we now have three ways to indicate null: the List's bits vector, the type vector in the union, and the bits vector in each element vector. Again, the writer assumes that if the List vector is null, the entire value for that row is null. The reader is again paranoid and handles all three nullable states. (Again, a huge mess.)

The readers can provide a nice API for these cases since we know the List format up front. They can present the list as either a nullable array of a single type, or as an array of unions.

Writers have more of a challenge. If we knew that a List was being used as a list of, say, Nullable Int, we could present the List as an array writer with Int elements. But, the List allows dynamic type addition, as with unions. (In the case of the list, it has internal special handling for the single vs. many type case.)

To isolate the client from the list representation, it is simpler to always present a List an array of variants. But, this is awkward in the single-type case. The solution is to use metadata. If the metadata promises to use only a single type, the writer can use the nullable array of X format. If the metadata says to use a union (the default), then the List is presented as an array of unions, even when the list has 0 or 1 member types. (The complexity here is excessive: Drill should really redesign this feature to make it simpler and to better fit the relational model.)

Vector Evolution

Drill uses value vector classes created during the rush to ship Drill 1.0. They are not optimal: the key value is that the vectors work.

The Apache Arrow project created a refined version of the vector classes. Much talk has occurred about ripping out Drill's implementation to use Arrow instead.

However, even Arrow has limits:

If we learn from the above, we might want to create a Value Vectors 2.0 based on the following concepts: Such changes would be a huge project if every operator continued to work directly with vectors and memory buffers. In fact, the cost would be so high that these improvements might never be done.

Therefore, a goal of this reader/writer layer is to isolate the operators from vector implementation. For this to work, the accessors must be at least as efficient as direct vector access. (They are now more efficient.)

Once all operators use this layer, a switch to Arrow, or an evolution toward Value Vectors 2.0 will be much easier. Simply change the vector format and update the reader and writer implementations. The rest of the code will remain unchanged. (Note, to achieve this goal, it is important to carefully design the accessor API [interfaces] to hide implementation details.)

Simpler Reader API

A key value of Drill is the ability for users to add custom record readers. But, at present, doing so is very complex because the developer must know quite a bit about Drill internals. At this level, they must know how to allocate vectors, how to write to each kind of vector, how to keep track of array sizes, how to set the vector and batch row counts, and more. In general, there is only one right way to do this work. (Though some readers use the old-style vector writers, others work with direct memory instead of with vectors, and so on.)

This layer handles all that work, providing a simple API that encourages more custom readers because the work to create the readers becomes far simpler. (Other layers tackle other parts of the problem as well.)

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