HI Aman,
I would completely agree with the analysis -- except for the fact that we can't
create a general solution, only a patchwork of incomplete ad-hoc solutions. The
question is not whether it would be useful to have a general solution (it
would), rather whether it is technically possible without some help from the
user (it is not, IMHO.)
I like the scenario presented, gives us a concrete example. Let's say an IoT
device produced files with an evolving schema. A field in a JSON file started
as BIGINT, later because DOUBLE, and finally became VARCHAR. What should Drill
do? Maybe the values are:
1
1.1
1.33
The change of types might represent the idea that the above are money amounts,
and the only way to represent values exactly is with a string (in JSON) and
with a DECIMAL in Drill.
Or, maybe the values are:
1
1.1
1.1rev3
Which showed that the value is a version string. Early developers thought to
use an integer, later they wanted minor versions, and even later they realized
they needed a patch value. The correct value type is VARCHAR.
Once can also invent a scenario in which the proper type is BIGINT, DOUBLE or
even TIMESTAMP.
Since Drill can't know the user's intention, we can invest quite a bit of
effort and still not solve the problem.
What is the alternative?
Suppose we simply let the query fail when we see a schema change, but we point
the user to a solution:
Query failed: Schema conflict on column `foo`: BIGINT and DOUBLE.
Use a schema file to resolve the ambiguity.
See
https://urldefense.proofpoint.com/v2/url?u=http-3A__drill.apache.org_docs_schema-2Dfile&d=DwIFaQ&c=cskdkSMqhcnjZxdQVpwTXg&r=PqKay2uOMZUqopDRKNfBtZSlsp2meGOxWNAVHxHnXCk&m=DOzeipgsStxUnotQemlm6judvWUdbAdPuvMBBYh4ilU&s=_ezJ4X476FCrf8ouHloYk1NLS91bs7ITW7u36molPmU&e=
for more information.
Now, the user is in control: we stated what we can and cannot do and gave the
user the option to decide on the data type.
This is a special case of other use cases: it works just as well for specifying
CSV types, refining JSON types and so on. A single solution that solves
multiple problems.
This approach also solves the problem that the JDBC and ODBC clients can't
handle a schema that changes during processing. (The native Drill client can,
which is a rather cool feature. xDBC hasn't caught up, so we have to deal with
them as they are.)
In fact, Drill could then say: if your data is nice and clean, query it without
a schema since the data speaks for itself. If, however, your data is messy (as
real-word data tends to be), just provide a schema to explain the intent and
Drill will do the right thing.
And, again, if the team tried the schema solution first, you'd be in a much
better position to see what additional benefits could be had by trying to guess
the type (and solving the time-travel issue.) (This is the lazy approach: do
the least amount of work...)
In fact, it may turn out that schema change as an issue disappears once users
have a nice, clean, well-understood solution -- the schema file.
Thanks,
- Paul
On Tuesday, November 6, 2018, 9:41:21 AM PST, Aman Sinha
<amansi...@gmail.com> wrote:
Hi Paul,
Thanks for the feedback ! I am in complete favor of doing the schema
discovery and schema hinting. But even on this list in the past we have
discussed other use cases such as IoT devices where the schema-on-read is
needed (I think it was in the context of the 'death of schema-on-read'
email thread). As I mentioned in my prior email, JSON document databases
don't have pre-defined schema and even if one does schema discovery, it
will have to be continuously updated given that these DBs are used in
operational applications where data is streaming in at a fast rate.
I think we should try for a complementary approach - wherever schema
discovery or hinting is feasible, Drill would use it. For others
scenarios, can we do a best effort and not fail the query ?
Note that I don't want to backtrack and revise the data types of the rows
already sent to the client. In fact, today, if you have 2 files with
different schema, if the columns are projected as below, the query will
return data to the client in separate batches. In fact, this is common
among Drill users to do data exploration (with a LIMIT clause).
(file 1: {a: 10, b: 20.5} file 2: {a: "cat", b: "dog"} )
0: jdbc:drill:zk=local> select a, b from dfs.`/tmp/table2` ;
*+------+-------+*
*| ** a ** | ** b ** |*
*+------+-------+*
*| *10 * | *20.5 * |*
*| *cat * | *dog * |*
*+------+-------+*
You mention 'drill cant' predict the future'..which is true and I am saying
we don't need to predict the future. If all operators did what the Scan
readers do which is emit a new record batch when it encounters a new
schema, then conceptually it would get us much farther along.
The point is : let's assume the client side is able to handle 2 different
schemas, how can Drill internally handle that in the execution plan ? For
the non-blocking operators it means that as soon as the schema changes, it
emits the previous Record Batch and starts a new output batch. For the
blocking operators, there's more things to take care of and I created
DRILL-6829
<https://urldefense.proofpoint.com/v2/url?u=https-3A__issues.apache.org_jira_browse_DRILL-2D6829&d=DwIFaQ&c=cskdkSMqhcnjZxdQVpwTXg&r=PqKay2uOMZUqopDRKNfBtZSlsp2meGOxWNAVHxHnXCk&m=DOzeipgsStxUnotQemlm6judvWUdbAdPuvMBBYh4ilU&s=im9AAhcJ6rXl9yqBRTVEP1pYgEvJ_etsmK_RB7yJcg4&e=>
to capture
that.
Aman
On Mon, Nov 5, 2018 at 8:50 PM Paul Rogers <par0...@yahoo.com.invalid>
wrote:
Hi Aman,
Thanks much for the write-up. My two cents, FWIW.
As the history of this list has shown, I've fought with the schema change
issue multiple times: in sort, in JSON, in the row set loader framework,
and in writing the "Data Engineering" chapter in the Learning Drill book.
What I have come to realize is that there is no general solution to the
schema change problem. Yes, there are clever things to do in special cases.
But he general problem is unsolvable.
Look at the open PR for the projection framework. There is an
implementation of a "schema smoother." It tries really hard, but it
highlights the inherent limitations of such an effort.
The key reason is that, do do a good job, rows processed now must know the
types of rows seen 100 million rows from now. Since Drill does not have a
time machine, that is not possible.
The easiest way to visualize this is with a single fragment that reads two
files. File A has 100K rows with column C as an Varchar. File B has 100K
rows with column C as an Int. There is no sort, so all rows are returned
directly to the client as, say, four 50K batches.
The client will encounter a schema with C as Varchar. Later, it will C as
Int. But, since the client already told the JDBC consumer that the type is
Varchar, the JDBC client is stuck. It could convert the Int to Varchar
behind the scenes.
Now, run the query again. The order in which Drill reads files is random.
Second time, the client sees C as an Int. Now, JDBC must convert the later
Varchar columns to Int. That works if the Varchar are numbers, but not if
the Ints should have been Varchar.
The general problem as I put it in the book, is that "Drill can't predict
the future" but that is precisely what is needed for a general solution.
However, if the user sets a policy (treat column C as a DECIMAL, even if
you read it as an Int or Varchar), then time travel is not necessary.
My humble suggestion is to focus on the schema effort: give the user a way
to define the resolution to the issue that is right for their data. See how
that works out for users. Then, with that extra information, go back and
see what other features might be useful.
The proposed schema support (at least as hints, preferably as a schema
file, full blown with a metastore) is a much better, easier to understand,
easier to explain solution that is familiar to anyone coming from a DB
background.
My suggestion: to understand the challenges and limitations, think through
many different scenarios: look at the history of this list for some, see
the notes in the Result Set Loader wiki and code for more. Work out how
they could be resolved. You may see something I've missed, or you may
realize that the problem is just not solvable in general without an
up-front schema.
More comments in the JIRA ticket.
Thanks,
- Paul
On Monday, November 5, 2018, 6:47:48 PM PST, Aman Sinha <
amansi...@gmail.com> wrote:
Hi all,
While we continue to enhance the schema provision and metastore aspects in
Drill, we also should explore what it means to be truly schema-less such
that we can better handle {semi, un}structured data, data sitting in DBs
that store JSON documents (e.g Mongo, MapR-DB).
The blocking operators are the main hurdles in this goal. I wrote some
thoughts on supporting Schema change in a Sort operator in DRILL-6829
<https://urldefense.proofpoint.com/v2/url?u=https-3A__issues.apache.org_jira_browse_DRILL-2D6829&d=DwIFaQ&c=cskdkSMqhcnjZxdQVpwTXg&r=PqKay2uOMZUqopDRKNfBtZSlsp2meGOxWNAVHxHnXCk&m=DOzeipgsStxUnotQemlm6judvWUdbAdPuvMBBYh4ilU&s=im9AAhcJ6rXl9yqBRTVEP1pYgEvJ_etsmK_RB7yJcg4&e=>
. Would welcome any
feedback and see how to go about it going forward.
Thanks,
Aman
