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From <he...@augerdata.com.au>
Subject Re: Successful (and not so successful) Production use cases for drill?
Date Mon, 24 Aug 2020 16:10:56 GMT
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
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

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:
> large enough to justify a distributed system, but not so large as to> 
> overtax the limited support Drill has for huge deployments.
> data is good, but not data that is dirty or with inconsistent format.
> 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
> 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
> 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"
> 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
> 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
> 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
> 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
> >> 
> >> 
> >> 
> >> 
> >> 
> > I've been working in reporting, data warehousing, business
> > data engineering(?) (the name of the field seems to rebrand every 5 or
> > years - or the lifecycle of 2 failed enterprise data projects - but
> > a> 
> > theory for another time) for a bit over 15 years now and for the last 5
> > 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
> > 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
> > 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
> > 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
> > using traditional on prem etl tools or the current data engineering
> > 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
> > as possible into a relational-like data store and leveraging MPP dbs
> > views and CTASes to create a conformed star schema. Then using the star>

> > schemas as building blocks create the complex (and actually useful)
> > Something like this can be up in a few weeks and still cover the
> > of> 
> > user facing features a full data pipeline/ETL would have (snapshots +> 
> > transactional facts, inferred members, type 1 dims only - almost
> > 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
> > 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
> > 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
> > is how it's actually being used by others. It would be nice if it could>

> > replace the MPP altogether..> 
> >> 
> >> 
> >> 
> >> 

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