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Drill >> mail # dev >> Schemaless Schema Management: Pass per record, Per batch, or ?


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Re: Schemaless Schema Management: Pass per record, Per batch, or ?
I don't have much to add to the options you've suggested, I do agree
storing the schema and sending the diffs will be the most ideal way to go.

And since we already need to look at every row, we can build the schema
diffs pretty easily.

I currently have a simple JSON -> MsgPack impl using Yajl here:
https://github.com/tnachen/incubator-drill/tree/executor/sandbox/executor

Depending on the parser we use, most already have basic types detection and
we can extend more data types discovery later on as extensions.

Tim

On Wed, Nov 14, 2012 at 3:17 PM, Jacques Nadeau <[EMAIL PROTECTED]>wrote:

> One of the goals we've talked about for Drill is the ability to consume
> "schemaless" data.  What this really means to me is data such as JSON where
> the schema of data could change from record to record (and isn't known
> until query execution).  I'd suggest that in most cases, the schema within
> a JSON 'source' (collection of similar files) is mostly stable.  The
> default JSON format passes this schema data with each record.  This would
> be the simplest way to manage this data.  However, if Drill operated in
> this manner we'd likely have to manage fairly different code paths for data
> with schema versus those without.  There also seems like there would be a
> substantial processing and message size overhead interacting with all the
> schema information for each record.  Couple of notes:
>
>    - By schema here I more mean the structure of the key names and nested
>    structure of the data as opposed to value data types...
>    - A simple example: we have a user table and one of the query
>    expressions is user.phone-numbers.  If we query that without schema, we
>    don't know if that is a scalar, a map or an array.  Thus... we can't
> figure
>    out the number of "fields" in the output stream.
>
>
> Separately, we've also talked before about having all the main executional
> components operating on a batches of records as a single work unit
> (probably in MsgPack streaming format or similar).
>
> One way to manage schemaless data within these parameters is to generate a
> concrete schema of data as we're reading it and sending it with each batch
> of records.  To start, we could resend it with every batch.  Later, we
> could add an optimization that the schema is only sent when it changes.  A
> nice additional option would be to store this schema stream as we're
> running the first queries so we can treat this data as fully schemaed on
> later queries.  (And also provide that schema back to whatever query parser
> is being used.)
>
> Thoughts?  What about thoughts on data types discovery in schemaless data
> such as JSON, CSV, etc?
>
> Jacques
>
NEW: Monitor These Apps!
elasticsearch, apache solr, apache hbase, hadoop, redis, casssandra, amazon cloudwatch, mysql, memcached, apache kafka, apache zookeeper, apache storm, ubuntu, centOS, red hat, debian, puppet labs, java, senseiDB