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BinaryObject API should be reworked, as it will not represent actual serialized objects anymore. It should be replaced with something like BinaryRecord or DataRecord representing a record in a cache or table. Similar to the current binary objects, records will provide access to individual fields. A record can also be deserialized into a class with any subset of fields represented in the record.

Introducing versioned schema allows to upgrade rows to the latest version on-fly and even to update a schema automatically in some simple cases, e.g. adding a new column. 
So, a user may choose between two modes: Strict and Live for manual schema management and dynamic schema expansion correspondingly.

Schema Definition API

There are several ways a schema can be defined. The initial entry point to the schema definition is SchemaBuilder java API:


The schema builder calls are transparently mapped to DDL statements so that all operations possible via a builder are also possible via DDL and vice versa.

Additionally, we may introduce an API that will infer the schema from a key-value pair using class fields and annotations. The inference happens on the calling site of the node invoking the table modification operation.

Table schema should be automatically exposed to the table configuration subtree so that simple schema changes are available via ignite CLI and the schema can be defined during the table creation via ignite CLI.

Data restrictions

The Schema-first approach imposes certain natural requirements which are more strict than binary object serialization format:


Flags BitsDescription
0, 1

VarTable formats:

  • (0, 0) - SKIPPED.  VarTable for chunk is omitted  (all column values in the chunk either of fix-sized type or null);
  • (0, 1) - TINY format (1 byte for offset), format_size = 1;
  • (1, 0) - MEDIUM format (2 bytes for offset), format_size = 2;
  • (1, 1) - LARGE format (4 bytes for offset), format_size = 4
Hash calculation and key comparison

Row hash can be calculated from affinity field values while marshaling to byte array. Because field order is defined by the scheme, a key hash can be calculated consistently regarding the column order.

Key can be compared as byte[] for compatible schemas (that have the same key column set), otherwise, the oldest row should be upgraded first.
It is possible to compare keys column-by-column regarding the schema if the same key can be serialized in more than one way. E.g. kind of compression will be supported and compressed rows could be marked with a flag.

Schema evolution

Unlike Ignite 2.x approach, where binary object schema ID is defined by a set of fields that are present in a binary object, for the schema-first approach we assign a monotonically growing identifier to each version of the cache schema. The ordering guarantees should be provided by the underlying metadata storage layer (for example, the current distributed metastorage implementation or consensus-based metadata storage). The schema identifier should be stored together with the data rows (but not necessarily with each row individually: we can store schema ID along with a page or larger chunks of data). The history of schema versions must be stored for a long enough period of time to allow upgrade all existing data stored in a given cache.

Given schema evolution history, a row migration from version N-k to version N is a straightforward operation. We identify fields that were dropped during the last k schema operations and fields that were added (taking into account default field values) and update the row based on the field modifications. Afterward, the updated row is written in the schema version N layout format. The row upgrade may happen on reading with an optional writeback or on the next update. Additionally, a row upgrade in the background is possible.

Since the row key hashcode is inlined to the row data for quick key lookups, we require that the set of key columns do not change during the schema evolution. In the future, we may remove this restriction, but this will require careful hashcode calculation adjustments since the hash code value should not change after adding a new column with default value. Removing a column from the key columns does not seem possible since it may produce duplicates, and checking for duplicates may require a full scan.

Additionally to adding and removing columns, it will be possible to allow column type migrations when type change is non-ambiguous (a type upcast, e.g. Int8 → Int16,  or by means of a certain expression, e,g, Int8 → String using CAST expression). Type conversions that narrow the column range (e.g. Int16 → Int8) must only be allowed using explicit expressions that will allow Ignite to validate that no RangeOutOfBoundsException is possible during the conversion.

For example, consider the following sequence of schema modifications expressed in SQL-like terms:

Code Block
CREATE TABLE Person (id INT, name VARCHAR(32), lastname VARCHAR(32), taxid int);
ALTER TABLE Person DROP COLUMN lastname, taxid;

This sequence of modifications will result in the following schema history


With this history, upgrading a row (1, "John", "Doe") of version 1 to version 4 means erasing columns lastname and taxid and adding columns residence with default "GB" and lastname (the column is returned back) with default "N/A" resulting in row (1, "John", "GB", "N/A").

Class-agnostic schema mapping

It's clear that given a fixed schema, we can generate an infinite number of classes that match the column of this schema. This observation can be used to simplify ORM for the end-users. For the APIs which return Java objects, the mapping from schema columns to the object fields can be constructed dynamically, allowing to deserialize a single row into instances of different classes.

For example, let's  say we have a schema PERSON (id INT, name VARCHAR (32), lastname VARCHAR (32), residence VARCHAR (2), taxid INT). Each row of this schema can be deserialized into the following classes:

Code Block
class Person {
    int id;
    String name;
    String lastName;
Code Block
class RichPerson {
    int id;
    String name;
    String lastName;
    String residence;
    int taxId;

For each table, a user may specify a default Java class binding, and for each individual operation a user may provide a target class for deserialization:

Code Block
Person p = table.get(key, Person.class);

Given the set of fields in the target class, Ignite may optimize the amount of data sent over the network by skipping fields that would be ignored during deserialization.

Update operation with object of truncated class is also possible, but missed fields will be treated as "not-set" as if it is done via SQL INSERT statement with some PERSON table fields missed. Missed field values will be implicitly set to DEFAULT column value regarding the row schema version.

Code Block

It may be impossible to insert an object/row with missed field if field is declared with NOT-NULL constraint and without DEFAULT (non-null) value specified.

Type mapping

Ignite will provide out-of-box mapping from standard platform types (Java, C#, C++) to built-in primitives. A user will be able to alter this mapping using some external mechanism (e.g. annotations to map long values to Number). Standard mapping is listed in the table below:


Java has no native support for unsigned types. We still can introduce 'unsigned' flag to schema type or separate binary type-codes, and allow to map to the closest types of wider range. E.g. map Uint8 → short and recheck constraints during serialization.

If one will try to serialize object with 'short' value out of Uint8 range then it end up with exception (ColumnValueIsOutOfRangeException).

Dynamic schema expansion (Live-schema)

One of the important benefits of binary objects was the ability to store objects with different sets of fields in a single cache. We can accommodate for a very similar behavior in the schema-first approach.

When a tuple is inserted into a table, we attempt to 'fit' tuple fields to the schema columns. If the tuple has some extra fields which are not present in the current schema, the schema is automatically updated to store additional extra fields that are present in the tuple.
This will work in the same way any Java objects that are first-citizens: e.g. Java object or objects in terms of other languages which has implementaion.


Risks and Assumptions