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From Paul Rogers <par0...@gmail.com>
Subject Re: Successful (and not so successful) Production use cases for drill?
Date Tue, 25 Aug 2020 07:32:06 GMT
Hi,

To answer the specific CSV questions:

Drill works with CSV files with or without headers. Without headers, Drill
use a positional array, which is fragile when columns change. With headers,
CSV uses column names, and so column order does not matter. Missing columns
are assumed to be VarChar, and so just show up as blank. Out of the box,
however, Drill makes no attempt to guess column types (everything is
VarChar).

There is a newer "provided schema" feature which can apply a schema to a
CSV file. (Not all formats support the schema, but CSV is one that does.)
So, if you know some or all column types, you can spell those out using a
CREATE SCHEMA statement (or manually create the file) to handle types.

Drill supports views, with views represented as text (actually, I think
JSON) files. So, no metadata; just extra text added to a SQL query. Any
dependency or lineage information would need to be added on top. (Might be
a good feature to add to Drill.)

Although Drill has always said it can handle messy data, when you get right
down to it, Drill is no better (or worse) than any other tool: if you give
it garbage data (inconsistent column names, inconsistent types, etc.)
you'll get garbage results. There is no magic.

CSV is pretty forgiving by itself. However, if you add types, (e.g. CAST a
column to an INT), and the data is instead "one", then the query will fail.
Drill does not attempt to discard the one record and continue. (That is
called "data loss" and is a P1 error, especially if we were to discard the
one record that really mattered.)

Drill supports partitions (directories). Only queries that touch the file
will fail. If your data is in subdirectories, and you only query the
"2020-02" directory, then bad data in the "2020-03" directory won't cause
the query to fail.

Interesting idea about doing ETL on each query. (That is, apply data
clean-up rules to each record as read.) The problems are 1) it will be slow
(and we all know that the only three things people care about are 1)
performance, 2) performance, and 3) performance), and 2) if the rules miss
anything, the user will just get a cryptic query failure with no good data
to help figure out which file and record caused the problem. (You can add
the into to a log message, but that is hardly user friendly.)

A hybrid solution is to load your data using Spark (or, at small scale,
Python) to clean up the wacky formats. Standardize into a single file
format. That is, classic ETL. For performance, that file wants to be
Parquet, not CSV. (That is, if you have to do clean-up, use that
opportunity to optimize for query performance on subsequent reads.) Then,
you can craft views on top of your clean data.

Drill provides both ODBC and JDBC drivers. Just about any tool that
supports those interfaces should work with Drill.

Thanks,

- Paul


On Mon, Aug 24, 2020 at 9:51 AM <hello@augerdata.com.au> wrote:

> Ha yes, big data is not quick data, especially if we’re talking about the
> psychological sub 5-8sec time limit to render a viz (so getting the data
> AND
> making it pretty). Any longer and the experience is poor. Most MPP
> platforms
> take longer to compile the query plan like you say!
>
> As a developer/analyst/integrator I want to easily and cheaply ingest flat
> files (csvs) and relational data sources into a single relational layer
> that
> supports complex SQL so I can write complex transformation views over the
> top with the option to materialise data for performance
>
> So there are 5 components;
> Easily get disparate data source types (flat file/rdbms) into a single
> 'logical' layer
> Have a thick sql client gui app (not web based, but an installed .exe) that
> has support for the drill and some of the metadata operations so I can
> quickly query the data, develop views, investigate technical issues, bulk
> generate DDL etc
> OK speed - not real time data viz rendering speed, but waiting 30 sec for a
> count (*) from local1000row.csv is crap
> Materialising data for improved performance - does drill have a preferred
> internal 'data format'? Or push data to a RDBMS via one of the plugins. Is
> this slow for bulk data volumes or does it leverage native bulk insert
> syntax rather than piping individual single-row "insert into" statements?
> Bulk reading data out of drill via odbc/jdbc (eg to import into a in
> memory/columnstore db for better data viz query responses)
>
>
> I’m more focused on the traditional financial and business operational data
> rather than massive web logs or raw streaming data
> Sources would be single files and tables in an rdbms up to 10M rows. A
> total
> “database” of under 1B rows.
> REST APIs aren’t a priority
>
> How forgiving is the csv parser?
> CSVs that slightly change month to month are my biggest manual time sink.
> Changes that to a human are trivial/inconsequential but fatal to a
>
> Eg can I have a "table" schema configured for a directory that has a number
> of csvs
> Orders202001.csv has columns |OrderID|ProductCode|Quantity|Date|
> Orders202002.csv has columns |OrderID|ProductCode|Date|Quantity|
>
> A single schema definition over the top of these two files should work
> correctly for both - the ordinal position in the individual csvs is
> dynamically mapped to the schema based on the csv header column
>
> The same applies to extra columns and missing columns
>
> Orders202003.csv has columns
> |OrderID|ProductCode|SupplierCode|Quantity|Date|
> Orders202004.csv has columns |ProductCode|Quantity|Date|
>
> Extra columns are ignored and missing columns show as null Throw a
> 'warning'
> or an option to set a column as mandatory in which case throw an error
>
> How does drill handle the situation where there are multiple csvs in a
> directory and one fails but the rest are ok. Is the whole table offline? Do
> all selects fail or does it show what it knows and throws a warning?
>
> I've written a c# csv handler like above and use for ETLing into a
> relational dbs when required. It saves so much time.
>
>
> Is there a 3rd party SQL query tool that plays nicely with drill?
>
>
> I do a lot of funky SQL with views on views and CTEs etc etc. How accurate
> is the dependency metadata? Would I be able to generate object level
> (view/table) data lineage/dependency data?
>
> As an aside – I’ve seen some of the threads om the mailing list about
> writing a generic rest plugin. I’ve previously used the CDATA -
> https://www.cdata.com/drivers/rest/odbc/ (worth a download of the trial to
> check out for ideas imho) especially mapping output data and uri params
> http://cdn.cdata.com/help/DWF/odbc/pg_customschemacolumns.htm
>
> On 2020/08/21 04:55:54, Paul Rogers <p...@gmail.com> wrote:
> > Hi, welcome to Drill.>
> >
> > In my (albeit limited) experience, Drill has a particular sweet spot:
> data>
> > large enough to justify a distributed system, but not so large as to>
> > overtax the limited support Drill has for huge deployments.
> Self-describing>
> > data is good, but not data that is dirty or with inconsistent format.
> Drill>
> > is good to grab data from other systems, but only if those systems have>
> > some way to "push" operations via a system-specific query API (and
> someone>
> > has written a Drill plugin.)>
> >
> > Drill tries to be really good with Parquet: but that is not a "source">
> > format; you'll need to ETL data into Parquet. Some have used Drill for
> the>
> > ETL, but that only works if the source data is clean.>
> >
> > One of the biggest myths around big data is that you can get
> interactive>
> > response times on large data sets. You are entirely at the mercy of I/O>
> > performance. You can get more, but it will cost you. (In the "old days"
> by>
> > having a very large number of disk spindles; today by having many nodes>
> > pull from S3.)>
> >
> > As your data size increases, you'll want to partition data (which is as>
> > close to indexing as Drill and similar tools get.) But, as the number
> of>
> > partitions (or, for Parquet, row groups) increases, Drill will spend
> more>
>
> > time figuring out which partitions & row groups to scan than it spends>
> > scanning the resulting files. The Hive Metastore tries to solve this,
> but>
>
> > has become a huge mess with its own problems.>
> >
> > From what I've seen, Drill works best somewhere in the middle: larger
> than>
> > a set of files on your laptop, smaller than 10's of K of Parquet files.>
> >
> > Might be easier to discuss *your* specific use case rather than explain
> the>
> > universe of places where Drill has been used.>
> >
> > To be honest, I guess my first choice would be to run in the cloud
> using>
> > tools available from Amazon, DataBricks or Snowflake if you have a>
> > reasonably "normal" use case and just want to get up and running
> quickly.>
>
> > If the use case turns out to be viable, you can find ways to reduce
> costs>
>
> > by replacing "name brand" components with open source. But, if you
> "failed>
> > fast", you did so without spending much time at all on plumbing.>
> >
> > Thanks,>
> >
> > - Paul>
> >
> >
> > On Thu, Aug 20, 2020 at 9:02 PM <he...@augerdata.com.au> wrote:>
> >
> > > Hi all,>
> > >>
> > >>
> > >>
> > > Can some of the users that have deployed drill in production, whether>
> > > small/medium and enterprise firms, share the use cases and
> experiences?>
>
> > >>
> > >>
> > >>
> > > What problems was drill meant to solve?>
> > >>
> > >>
> > >>
> > > Was it successful?>
> > >>
> > >>
> > >>
> > > What was/is drill mostly used for at your corporation?>
> > >>
> > >>
> > >>
> > > What was tried but wasn't taken up by users?>
> > >>
> > >>
> > >>
> > > Has it found a niche, or a core group of heavy users? What are their
> roles?>
> > >>
> > >>
> > >>
> > >>
> > >>
> > > I've been working in reporting, data warehousing, business
> intelligence,>
> > > data engineering(?) (the name of the field seems to rebrand every 5 or
> so>
> > > years - or the lifecycle of 2 failed enterprise data projects - but
> that's>
> > > a>
> > > theory for another time) for a bit over 15 years now and for the last 5
> or>
> > > so have been trying to understand why 70-80% of projects never
> achieve>
> > > their>
> > > aims. It doesn't seem to matter if they're run by really smart (and>
> > > expensive!) people using best in class tools and processes. Their
> failure>
> > > rate might be closer to the 70%, but that's still pretty terrible>
> > >>
> > >>
> > >>
> > > I have a couple theories as to why and have tested them over the last 5
> or>
> > > so years>
> > >>
> > >>
> > >>
> > > One part is reducing the gap between project inception and production>
> > > quality data output. Going live quickly creates enthusiasm + a
> feedback>
>
> > > loop>
> > > to iterate the models which in turn creates a sense of engagement>
> > >>
> > >>
> > >>
> > > Getting rid of a thick ETL process that takes months or more of dev
> and>
>
> > > refactoring before hitting production is one component. Using ~70% of
> the>
> > > project resources on the plumbing - leaving very little for the
> complex>
>
> > > data>
> > > model iterations - just creates a tech demo not a commercially useful>
> > > solution.  I don't think this is a technology problem, and applies
> whether>
> > > using traditional on prem etl tools or the current data engineering
> scripts>
> > > and cron jobs but in the cloud>
> > >>
> > >>
> > >>
> > > The least unsuccessful data engineering approach I've seen is the ELT>
> > > logical data mart pattern; landing the source data as close to a 1:1
> format>
> > > as possible into a relational-like data store and leveraging MPP dbs
> via>
> > > views and CTASes to create a conformed star schema. Then using the
> star>
>
> > > schemas as building blocks create the complex (and actually useful)
> models.>
> > > Something like this can be up in a few weeks and still cover the
> majority>
> > > of>
> > > user facing features a full data pipeline/ETL would have (snapshots +>
> > > transactional facts, inferred members, type 1 dims only - almost
> everyone>
> > > double joins a type 2 dim to get the current record anyway). While
> they>
>
> > > aren't always (or even usually) 100% successes they at least have
> something>
> > > useful or just fail quickly which is useful in itself>
> > >>
> > >>
> > >>
> > > The first part of this - getting all the data into a single spot,
> still>
>
> > > sucks and is probably more fiddly than 10 years ago because it's all
> flat>
> > > files and apis now vs on premise db->db transfers>
> > >>
> > >>
> > >>
> > > This is where I *think* drill might help me, but just want to check if
> this>
> > > is how it's actually being used by others. It would be nice if it
> could>
>
> > > replace the MPP altogether..>
> > >>
> > >>
> > >>
> > >>
> >
>
>

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