Alteryx Designer Desktop Ideas

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.

Hello,

As I mentioned in this previous idea : https://community.alteryx.com/t5/Alteryx-Designer-Ideas/Generic-In-database-connection-please-stop-i...

field mapping in generic in-db connection is based on Microsoft Sql Server. Given the specificity of MSQL Server field types, I would like to change that in order to at least be able to use another database. Without that, this feature has no sense at all.

Best regards,

Simon

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. 

It would be awesome if there was a cross tab in DB option because right now I have to stream out millions of records to build a cross tab.

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,

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,


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

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.

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!

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 *).

Alteryx doesnt support querying tables within Apache Ignite via Ignite ODBC connector. Connectivity from Ignite being an in memory database with Alteryx would help in better connectivity via ODBC.

https://apacheignite-sql.readme.io/docs/overview 

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.

The idea is to store credentials, login/pw in a "credential alias".

Then, those credential aliases can be used in :

-traditional aliases/connection

-in database aliases/connection

-hdfs aliases/connection

-API

-on user aliases for connected controllers/gallery

...etc.

The idea is that I only have to change the credentials once for all the connection type (on Hive, I have the in db alias, the traditional alias and even an HDFS alias using exactly the same credentials !! and I have to change all that manually).

Hi there Alteryx team,

When we load data from raw files into a SQL table - we use this pattern in almost every single loader because the "Update, insert if new" functionality is so slow; it cannot take advantage of SSVB; it does not do deletes; and it doesn't check for changes in the data so your history tables get polluted with updates that are not real updates.

This pattern below addresses these concerns as follows:

- You explicitly separate out the inserts by comparing to the current table; and use SSVB on the connection - thereby maximizing the speed

- The ones that don't exist - you delete, and allow the history table to keep the history.

- Finally - the rows that exist in both source and target are checked for data changes and only updated if one or more fields have changed.

Given how commonly we have to do this (on almost EVERY data pipe from files into our database) - could we look at making an Incremental Update tool in Alteryx to make this easier?    This is a common functionality in other ETL platforms, and this would be a great addition to Alteryx.

The Tableau Hyper API supports regular SQL queries, see https://help.tableau.com/current/api/hyper_api/en-us/reference/sql/index.html and https://help.tableau.com/current/api/hyper_api/en-us/docs/hyper_api_reference.html for more information. Being able to use the In-database tools for querying Hyper would let us take advantage of Hyper's internal optimizations just like other databases.

DELETE from Source_Data Where ID in

SELECT ID from My_Temp_Table where FLAG = 'Y'

.... 

Essentially, I want to update a DB table with either an update or with the deletion of rows.  I can't delete all of the data.  My work around will be to create/insert into a table the keys that i want to delete and try to use a input/output tool with SQL that performs the delete.  Any other suggestions are welcome, but a tool is best.

Thanks,

Mark