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From "Vivek Ratan (JIRA)" <j...@apache.org>
Subject [jira] Commented: (HADOOP-1883) Adding versioning to Record I/O
Date Mon, 17 Sep 2007 07:03:34 GMT

    [ https://issues.apache.org/jira/browse/HADOOP-1883?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#action_12527961
] 

Vivek Ratan commented on HADOOP-1883:
-------------------------------------

Let me answer #2 first. Remember, with versioning, we're looking to figure out which fields
have changed. You need both a field name and its type to uniquely identify a field. Suppose
we have: 
{code}
class a{
  int s;
  char c;
}
{code}

Now, suppose I replace the second field: 
{code}
class a{
  int s;
  long c;
}
{code}

A deserializer generated from the new class, when reading code serialized by the old class,
needs to understand that the second field in the serialized data needs to be skipped, i.e.,
'char c' is different from from 'long c'. Similarly, if I changed just the name of a field,
and not its type, it is a different field (this one is more debatable, but it's safer to assume
that it's a different field). So the type information needs to contain both the field name
and field type, which a deserializer matches with the field name and type of its own fields
to see what to read and what to skip. [BTW, your example is true in that the class is invalid
because both fields have the same name, but I think it's the wrong example for what we're
discussing]. 

Now, alternatively, I could have the user assign a unique field number to each field (which
Thrift and Sawzall do). I could have something like this: 
{code}
class a{
  1: int s;
  2: char c;
}
{code}
If a user changes a field, they decide whether the field number changes. They SHOULD change
the field number if the field type changes (and maybe if only the name changes). Any deserializer
will depend on the user-generated field numbers to decide what fields to read and what to
skip. So, for example, you could change the class as follows: 
{code}
class a{
  1:int s;
  3:long c;
}
{code}
Here, the changed field has been given a new field number. 

Either approach works. The latter (using field numbers) is more space-efficient as we just
need to keep track of field numbers (and types, in order to know how many bytes to skip).
But it requires manual user support and is more prone to errors. We're recommending using
the former as the space inefficiency is minuscule (since you presumably will store the type
information once, for hundreds and thousands of records), and you don't have to change DDLs
and depend on users generating field numbers. 


> Adding versioning to Record I/O
> -------------------------------
>
>                 Key: HADOOP-1883
>                 URL: https://issues.apache.org/jira/browse/HADOOP-1883
>             Project: Hadoop
>          Issue Type: New Feature
>            Reporter: Vivek Ratan
>
> There is a need to add versioning support to Record I/O. Users frequently update DDL
files, usually by adding/removing fields, but do not want to change the name of the data structure.
They would like older & newer deserializers to read as much data as possible. For example,
suppose Record I/O is used to serialize/deserialize log records, each of which contains a
message and a timestamp. An initial data definition could be as follows:
> {code}
> class MyLogRecord {
>   ustring msg;
>   long timestamp;
> }
> {code}
> Record I/O creates a class, _MyLogRecord_, which represents a log record and can serialize/deserialize
itself. Now, suppose newer log records additionally contain a severity level. A user would
want to update the definition for a log record but use the same class name. The new definition
would be:
> {code}
> class MyLogRecord {
>   ustring msg;
>   long timestamp;
>   int severity;
> }
> {code}
> Users would want a new deserializer to read old log records (and perhaps use a default
value for the severity field), and an old deserializer to read newer log records (and skip
the severity field).
> This requires some concept of versioning in Record I/O, or rather, the additional ability
to read/write type information of a record. The following is a proposal to do this. 
> Every Record I/O Record will have type information which represents how the record is
structured (what fields it has, what types, etc.). This type information, represented by the
class _RecordTypeInfo_, is itself serializable/deserializable. Every Record supports a method
_getRecordTypeInfo()_, which returns a _RecordTypeInfo_ object. Users are expected to serialize
this type information (by calling _RecordTypeInfo.serialize()_) in an appropriate fashion
(in a separate file, for example, or at the beginning of a file). Using the same DDL as above,
here's how we could serialize log records: 
> {code}
> FileOutputStream fOut = new FileOutputStream("data.log");
> CsvRecordOutput csvOut = new CsvRecordOutput(fOut);
> ...
> // get the type information for MyLogRecord
> RecordTypeInfo typeInfo = MyLogRecord.getRecordTypeInfo();
> // ask it to write itself out
> typeInfo.serialize(csvOut);
> ...
> // now, serialize a bunch of records
> while (...) {
>    MyLogRecord log = new MyLogRecord();
>    // fill up the MyLogRecord object
>   ...
>   // serialize
>   log.serialize(csvOut);
> }
> {code}
> In this example, the type information of a Record is serialized fist, followed by contents
of various records, all into the same file. 
> Every Record also supports a method that allows a user to set a filter for deserializing.
A method _setRTIFilter()_ takes a _RecordTypeInfo_ object as a parameter. This filter represents
the type information of the data that is being deserialized. When deserializing, the Record
uses this filter (if one is set) to figure out what to read. Continuing with our example,
here's how we could deserialize records:
> {code}
> FileInputStream fIn = new FileInputStream("data.log");
> // we know the record was written in CSV format
> CsvRecordInput csvIn = new CsvRecordInput(fIn);
> ...
> // we know the type info is written in the beginning. read it. 
> RecordTypeInfo typeInfoFilter = new RecordTypeInfo();
> // deserialize it
> typeInfoFilter.deserialize(csvIn);
> // let MyLogRecord know what to expect
> MyLogRecord.setRTIFilter(typeInfoFilter);
> // deserialize each record
> while (there is data in file) {
>   MyLogRecord log = new MyLogRecord();
>   log.read(csvIn);
>   ...
> }
> {code}
> The filter is optional. If not provided, the deserializer expects data to be in the same
format as it would serialize. (Note that a filter can also be provided for serializing, forcing
the serializer to write information in the format of the filter, but there is no use case
for this functionality yet). 
> What goes in the type information for a record? The type information for each field in
a Record is made up of:
>    1. a unique field ID, which is the field name. 
>    2. a type ID, which denotes the type of the field (int, string, map, etc). 
> The type information for a composite type contains type information for each of its fields.
This approach is somewhat similar to the one taken by [Facebook's Thrift|http://developers.facebook.com/thrift/],
as well as by Google's Sawzall. The main difference is that we use field names as the field
ID, whereas Thrift and Sawzall use user-defined field numbers. While field names take more
space, they have the big advantage that there is no change to support existing DDLs. 
> When deserializing, a Record looks at the filter and compares it with its own set of
{field name, field type} tuples. If there is a field in the data that it doesn't know about
it, it skips it (it knows how many bytes to skip, based on the filter). If the deserialized
data does not contain some field values, the Record gives them default values. Additionally,
we could allow users to optionally specify default values in the DDL. The location of a field
in a structure does not matter. This lets us support reordering of fields. Note that there
is no change required to the DDL syntax, and very minimal changes to client code (clients
just need to read/write type information, in addition to record data). 
> This scheme gives us an addition powerful feature: we can build a generic serializer/deserializer,
so that users can read all kinds of data without having access to the original DDL or the
original stubs. As long as you know where the type information of a record is serialized,
you can read all kinds of data. One can also build a simple UI that displays the structure
of data serialized in any generic file. This is very useful for handling data across lots
of versions. 

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