Alteryx Designer Desktop Ideas

Alteryx has the ability to connect to data sources using fat clients and ODBC but not JDBC.  If the ability to use JDBC could be added to the product it could remove the need to install fat clients.

Currently, when one uses the Google BigQuery Output tool, the only options are to create a table, or append data to an existing table.  It would be more useful if there was a process to replace all data in the table rather than appending. Having the option to overwrite an existing table in Google BigQuery would be optimal.

Hello,


As of today, when you connect o, a database, you go through a batch of queries to retrieve which database it is ( cf https://community.alteryx.com/t5/Alteryx-Designer-Ideas/Smart-Visual-Query-Builder-for-in-db-less-te... where I suggest a solution to speed up the process) and then, Alteryx queries the metadata. In order to get the column in each table, Alteryx use a SHOW TABLES and then loop on each table. This is really slow. 

However, since Hive 3.0, an information_schema with the list of columns for each  table is now available. I suggest to use the information_schema.columns instead of the time-consuming loop.

PS : I don't know if it's linked to the Active Query Builder, the third-party tool behind the Visual Query Builder. In that case, it would be a good idea to update it as suggested here https://community.alteryx.com/t5/Alteryx-Designer-Ideas/Update-Query-Builder-component/idi-p/799086

Best regards,

Simon

Hello,

As of today, the in db connexion window is divided into :
-write tab

-read tab

However, writing means two different thing : inserting and in-db writing. Alteryx has already 2 different tools (Data Stream In and Write Data).

Si what I propose is to divide the window into :
-read
-write
-insert

Best regards,

Simon

Hello,

Here a use case :

I work on the projects A and B with Alteryx inj IN DB mode.

My coworker works only on project B and have no rights to the data of project A.

When using temporary table in Alteryx, we both create the temporary tables in the default database. The issue is my coworker can see my temporary data of project A, which is not safe.

Solution : allow me to specify the database/schema when I create my temporary table.

Hi,

     Currently loading large files to Postgres SQL(over 100 MB) takes an extremely long time. For example writing a 1GB file to Postgres SQL takes 27 minutes! This is serious impacting our ability to use Alteryx as an ETL tool for loading our target Postgres Data Warehouse. We would really like to see the bulk load capacity to Postgres supported by Alteryx to help alleviate the performance issues.

Thanks,

Vijaya

Hello all,

A few weeks ago Alteryx announced inDB support for GBQ. This is an awesome idea, however to make it run, you should use Oauth2 Authentication means GBQ API should be enabled. As of now, it is possible to use Simba ODBC to connect GBQ. My idea is to enhance the connection/authentication method as we have today with Simba ODBC for Google BigQuery and support inDB. It is not easy to implement by IT considering big organizations, number of GBQ projects and to enable API for each application. By enhancing the functionality with ODBC, this will be an awesome solution.

Thank you for voting

Albert

Hi all,
Something really interesting I found - and never knew about, is there are actually in-DB predictive tools. You can find these by having a connect-indb tool on the canvas and dragging on one of the many predictive tools.

For instance:
boosted model dragged on empty campus:

Boosted model tool deleted, connect in-db tool added to the canvas:

Boosted Model dragged onto the canvas the exact same:

This is awesome! I have no idea how these tools work, I have only just found out they are a thing. Are we able to unhide these? I actually thought I had fallen into an Alteryx Designer bug, however it appears to be much more of a feature.

Sadly these tools are currently not searchable for, and do not show up under the in-DB section. However, I believe these need to be more accessible and well documented for users to find.

Cheers,
TheOC

Sometimes I need to connect to the data in my Database after doing some filtering and modeling with CTEs. To ensure that the connection runs quicker than by using the regular input tool, I would like to use the in DB tool. But is doesn't working because the in DB input tool doesn't support CTEs. CTEs are helpful for everyday life and it would be terribly tedious to replicate all my SQL logic into Alteryx additionally to what I'm already doing inside the tool. 

I found a lot of people having the same issue, it would be great if we can have that feature added to the tool. 

It would be great to have the below functionality in Alteryx.

A workflow is built in Alteryx and button click in Alteryx can be used to generate SQL code that can be ran on a specific database platform, such as SQL Server to run external editors such as SQL Server Management Studio. Thanks. 

The Transpose In-db stands in the "Laboraty" for years now. I understand Alteryx invested some time and money to develop that but sadly we still can't use that tool for sensitive workflows. Did you get some bugs on it? Can you please correct it and make this tool an "official" tool?

Thanks

Hello,

My issue is very easy to solve. I want to use the generic ODBC In database for a specific base (monetdb here but it isn't important).

The connexion works just fine. However, I cannot create table because the data types are changed and does not even exist. Here is my data with some Date type :

And here the error in my data stream in give me this very interesting message  :

Error: Entrée du flux de données (2): Erreur lors de la création de la table "formation.temp1" : [MonetDB][ODBC Driver 11.31.11]Type (datetime) unknown in: "create  table "formation"."temp1" ("AccountNumber" varchar(255),"BillToAddressID"
syntax error, unexpected IDENT in: ""Freight""

CREATE  TABLE "formation"."temp1" ("AccountNumber" varchar(255),"BillToAddressID" float,"BusinessEntityID" float,"Comment
" float,"CreditCardApprovalCode" varchar(255),"CreditCardID" float,"CurrencyRateID" float,"CustomerID" float,"DueDate" datetime,"Freight" real,"OnlineOrderFlag" float,"OrderDate" datetime,"OrderDate_Month" varchar(255),"OrderDate_Year" float,"PurchaseOrderNumber" varchar(255),"RevisionNumber" float,"SalesOrderID" float,"SalesOrderNumber" varchar(255),"ShipDate" datetime,"ShipMethodID" float,"ShipToAddressID" float,"Status" float,"SubTotal" float,"TaxAmt" float,"TotalDue" float)

1/ My field is a date, why do you want to convert it in Datetime??

2/ Datetime is not even a usual field type in sql database (at least not supported by monetdb, vertica, postgresql, oracle, etc, etc...)... it should obviously be timestamp

Currently, this non-specific in database ODBC connexion cannot be used at all!

TIBCO Data Virtualization is a Data Virtualization product focused on creating a virtual data store consolidating data from throughout the enterprise.  It can be accessed via a SQL query engine, and has a variety of supported connectors, including an ODBC driver.

This data source can be connected to via ODBC in Alteryx today, but error messaging is unclear/unhelpful, and attempting to use the Visual Query Builder causes Alteryx to crash.

Adding TIBCO Data Virtualization as a supported ODBC connection would empower business users to leverage this product and easily utilize this enterprise data store, enhancing the value of the Alteryx platform as a consumer of this data.

As simple as the title : an In-Database Block Until Done would be a pretty nice feature to control the execution of a workflow.

From Wikipedia

Druid is a column-oriented, open-source, distributed data store written in Java. Druid is designed to quickly ingest massive quantities of event data, and provide low-latency queries on top of the data.[1] The name Druid comes from the shapeshifting Druid class in many role-playing games, to reflect the fact that the architecture of the system can shift to solve different types of data problems.

Druid is commonly used in business intelligence/OLAP applications to analyze high volumes of real-time and historical data.[2] Druid is used in production by technology companies such as Alibaba,[2] Airbnb,[2] Cisco,[3] eBay,[4] Netflix,[5] Paypal,[2], Yahoo.[6] and Wikimedia Foundation [7] 

More and more companies are going from Hive to Druid for Dataviz needs, maybe it's time to look for Druid Integration with Alteryx?

I reported this to the support team but was told it was by design and to post here.

In-DB Inefficient SQL

I would like to report that the In-DB tools are generating horribly inefficient SQL code for simple operations.  It seems no matter what tools you use every statement is starting with a nested 'Select * From'.

Example Simple workflow:

This is a simple Select and Group by but the SQL Generated is:

SELECT "ShipTo", "ShipTo_Name", SUM("ECM_3PL_OVERHEADS_Unit") AS "Sum_ECM_3PL_OVERHEADS_Unit"

FROM (SELECT * FROM "_SYS_BIC"."shell.app.gsap.FL000_LSC.FL002_CTS.INT.RPT/CA_CTS_RPT_MAIN_001") AS "a"

GROUP BY "ShipTo", "ShipTo_Name"

This is taking a very long time to execute:

Statement 'SELECT "ShipTo", "ShipTo_Name", SUM("ECM_3PL_OVERHEADS_Unit") AS "Sum_ECM_3PL_OVERHEADS_Unit" FROM ...'

successfully executed in 15.752 seconds  (server processing time: 15.699 seconds)

Whereas if I take the same query and remove the nested Select *:

SELECT "ShipTo", "ShipTo_Name", SUM("ECM_3PL_OVERHEADS_Unit") AS "Sum_ECM_3PL_OVERHEADS_Unit"

FROM "_SYS_BIC"."shell.app.gsap.FL000_LSC.FL002_CTS.INT.RPT/CA_CTS_RPT_MAIN_001" AS "a"

GROUP BY "ShipTo", "ShipTo_Name"

It is very quick:

Statement 'SELECT "ShipTo", "ShipTo_Name", SUM("ECM_3PL_OVERHEADS_Unit") AS "Sum_ECM_3PL_OVERHEADS_Unit" FROM ...'

successfully executed in 1.211 seconds  (server processing time: 1.157 seconds)

So Alteryx is generating queries up to x13 slower than they should be thereby defeating the point of using In-DB.  As you can imagine in a workflow where we have multiple Connect In-DB tools this is a really substantial amount of time.  Example used above is from SAP HANA DB has 1.9m rows and ~90 columns but we have much bigger tables/views than this.

If you look you will see its same behaviour for all In-DB tools where each tool creates another nested Select with its particular operator.

MY SUGGESTION:

So my suggestion is that Alteryx should combine the SQL of the first few tools and avoid using SELECT * completely unless no Select tools have been used.  So it should combine:

- Connect In-DB + Select

- Connect In-DB + Filter

- Connect In-DB + Summarise

Preferably it should combine/flatten everything up until the first join or union.  But Select + Filter are a must!

Note it seems some DB's can cope OK with un-nesting these big nested queries in the query plans for some Tables but normally not for Views.  But some cannot cope at all and so the In-DB tools cannot even be used to Browse 100 records (due to select *).

Hello all,

From https://www.sqltutorial.org/sql-triggers/

Introduction to SQL Triggers

A trigger is a piece of code executed automatically in response to a specific event occurred on a table in the database.

A trigger is always associated with a particular table. If the table is deleted, all the associated triggers are also deleted automatically.

A trigger is invoked either before or after the following event:

• INSERT – when a new row is inserted
• UPDATE – when an existing row is updated
• DELETE – when a row is deleted.

When you issue an INSERT, UPDATE, or DELETE statement, the relational database management system (RDBMS) fires the corresponding trigger.

In some RDMBS, a trigger is also invoked in the result of executing a statement that calls the INSERT, UPDATE, or DELETE statement. For example, MySQL has the LOAD DATA INFILE, which reads rows from a text file and inserts into a table at a very high speed, invokes the BEFORE INSERT and AFTER INSERT triggers.

On the other hand, a statement may delete rows in a table but does not invoke the associated triggers. For example, TRUNCATE TABLE statement removes all rows in the table but does not invoke the BEFORE DELETE and AFTER DELETE triggers.



So basically, I would like to create some triggers from in db tools in Alteryx.

Best regards,

Simon

Where it stands now, only a file input tool can be used to pull data from Google BigQuery tables. The issue here is that the data is streamed and processed locally, meaning the power of BigQuery processing isn't actually being leveraged.

Adding BigQuery In-Database as a connection option would appeal to a wide audience. BigQuery is also standard SQL compliant with the SQL 2011 standard, so this may make for an even easier integration.

Hello all,

Some Database, including Hive, support natively scheduled queries (yes, the scheduling configuration is inside the database, not through etl/dataprep system). I think this would be an interesting feature for in-db workflow output : you play the worflow once and then only have to run it when it changes, the database do the scheduling. 



https://cwiki.apache.org/confluence/display/Hive/Scheduled+Queries

Intro

Executing statements periodically can be usefull in

• Pulling informations from external systems
• Periodically updating column statistics
• Rebuilding materialized views
  

Best regards,

Simon