Alternative approach of using Insert/Update Step for Upsert in PDI

What is an UPSERT?

UPSERT is a combination of two activities in a table i.e. Update and Insert based upon a unique key(Unique iD). A Relational database uses MERGE JOIN to perform UPSERT operation on data where it updates if there is any change on the existing data and INSERTS if there is a new data based upon certain conditions (identified as unique ID).

How UPSERT works in an ETL?

While loading the data from the source to target, there are two prominent approach implemented. Namely,

  • Truncate and load: Flush out the old data and reload the new data.
  • Insert/Update (Upsert): Update the existing record and Insert if there is a new data. It matches on a particular key or a set of keys to perform Update/Insert. If the keys doesn’t match, then it is an INSERT or else it will update. SCD performs in the same way(SCD-I is an UPSERT whereas SCD-II is an INSERT).

Truncate and load approach are not recommended in some scenarios where the source data only has current data. In case of maintaining historical information as well, we use UPSERT mechanism.

UPSERT in Pentaho Data Integration(PDI) :

There are various components that are used to implement UPSERT in PDI. These components use the unique key as the keys for lookup from the target table and specific operation is performed based upon the condition. The following are the components used for Upsert

  1. Insert/Update(Insert when there is no match and update if there is match)
  2. Dimensional Lookup/Update(This is used to SCD Operations)
  3. Synchronise after Merge(This step needs a flag to execute. Based upon the flags it will insert/update)

The basic algorithm/flow of how the UPSERT functions in PDI is as follows,


Disadvantages on the above steps :

Now the components that are present with PDI and the algorithms designed to implement the UPSERT is very slow while handling large amounts of data.

  1. The Insert/Update step does a lookup on all the values in the table and does one to one comparison to either insert or update. If the amount of data is huge, then the component will work very slowly and hence the whole process slows down.
  2. Similar case happens on the dimensional lookup/update and also on the Synchronise after merge step as well. Since it does a lookup on the whole table i.e. it compares with each and every data on the table, it slows down the process.

Alternative approach for the UPSERT technique:

There are certain scenarios where the ETL runs on a regular basis let’s say, on every 12 hours. In that case, the components used to implement the UPSERT technique wont help since they are very slow and if there are huge amounts of data, then its performance will be very slow. So there is a need of optimization.  Optimizing an ETL process  is one of the most important aspects that we need to undertake since it helps in faster processing. The following diagram will help you understand the alternate approach for UPSERT mechanism,


This steps mentioned in the above diagram works exactly like the Insert/Update component but with some minor tweaks. The following are the description of each step,

  1. Table Input: Taking input from the Source data that needs to be loaded into the Target.
  2. Add Sequence: The Sequence number that needs to be added as a business key to the table(This can change according to your requirement).
  3. Update: This component works only when there is a need for Update. If there is a new data, this component will fail since it only works when there is a need to update on the existing data.
  4. Table Output: PDI has a functionality that supports error handling of each component. The error handling component of the Update step will bring you to the Table Output component which will INSERT the new data into the target table.

Please see the below diagram for more better understanding,



This approach is faster than the usual Insert/update step since it doesn’t need to lookup if there is a new data. It will directly insert the new data when the Update component fails.

Try once and share your feedback!! 🙂



Nitish Mishra

Guide to Slowly Changing Dimensions [Intro + Type 1]

Guide to Slowly Changing Dimensions [Intro + Type 1]

Firstly what is a dimension?
A dimension is a structure that categorizes facts and measures which can be used to understand business requirements.

What is a Slowly Changing Dimension?
A Slowly Changing Dimension are dimensions that change slowly over time.

Why is it needed?
In Data Warehousing which deals with historical data, tracking of changes is important as it helps to give a better understanding of the Business.

An Example
Imagine having a customer dimension in a taxation department which holds records of address and so on,
and a customer changes his/her address it is important to track the address changes ie from old to new.

Types of Slowly Changing Dimension.
1. Type 1
2. Type 2
3. Type 3
4. Type 4
5. Type 5

1. Type 1:
This follows the technique of replacing the previous value, here no Historic data is/are kept, commonly used in correction or updating of records.

In this blog i would be showing you how to create a Type 1 Slowly Changing Dimension using Pentaho Data Integration:

1. Identify your source or create one if you have to.
CSV input

2. Load Data into the Database table
Load Data

Now Open a New transformation
3. Select a input component for your source in my case its a CSV input
4. Select a Get System Info input Component [Optional]
Double Click on it and
For Type: Select System Date (fixed)
For Name: load_data (Any Name)

5. If you are using a Get System info then this step is necessary
Select a Input Component Join Rows (Cartesian Product)
What this does is it makes a cross join of the new Load Date column to the Table or source table.
Select OK

6. Select a Select Values Input component,
this is used to add or remove or alter fields in the source before going to the target if required.

Note Make Sure all the components are connected as Shown below:
Before insert
Double Click on Select Values Component

Click on Get fields to select
Click on Meta-data and again select Get Fields to change
Click OK!

7. Select a insert/update Input component
Select your target Schema
Select your target Table

Now Select get fields
and remove the fields you don’t require while leaving the main key or primary key to look up values.
in my case client Id


Select Get Update Fields
And in the update column i will want Select N for the column i don’t want to update.
in my case again “client_id”

Now if selected the Get System info component to have a load date column then the below step is important

Remember we added load_date we want add that column

so Select SQL

look through the pre-defined query available and you will see a Alter Table to add column load_date is present
so all we need to do is Select Execute and run a query to see if your column has been added as shown below

load_date added

Click OK!

8. Now Go to your Source and edit a data and add a data also as shown below

and then Save it

I have added a new row and also editied Sohail’s region from NY to NJ

Now your all transformation should something like this with all the conncetions pointing to the right component

after all

Now Run the Transformation

Now after running successfully

Check your data via query

final data

As you can see data has being replaced/overwrite for Sohail [NY to NJ]
and also added 1 more new row Data.

In My next blog we will discuss Type 2 of Slowly Changing Dimension

Izebhijie Sohail Ehizogie

Slowly Changing Dimension in Pentaho Data Integration(Kettle)

Slowly Changing Dimension in Pentaho Data Integration(Kettle)

Slowly changing dimension(SCD) is a common mechanism in Datawarehousing concepts. The exact definition of SCD is the dimension that changes slowly over a time rather than on a regular schedule. In Data Warehouse there is a need to track changes in dimension attributes in order to report historical data. In other words, implementing one of the SCD types should enable users assigning proper dimension’s attribute value for given date. There are various approaches to deal with the data using SCD. The most commonly used approaches are:

1. SCD Type-I : Update the existing record(Overwrite)

2. SCD Type-II: Creates a new record and set the flag of the new record(historical).

3. SCD Type- III: Creates a new column which keeps the last updated record. Here the history is limited.

In kettle, there are components through which we can implement SCD on the dimension. One such component is the dimensional lookup/update.


The dimension lookup/update component allows to perform the Type-I and Type-II approach of SCD.


Keys:The keys are used to lookup the values with the destination table(dimension).

Technical key: It is basically the surrogate key which will be created if a new record is found.


Fields: The fields columns are the fields that are present on the dimension table on which you want to perform operations.A number of optional fields (in the “Fields” tab) are automatically managed by the step. You can specify the table field name in the “Dimension Field” column.

Type of Dimension update:

Insert: This is SCD-II mechanism where the a new row is inserted if changes are found based on the lookup. If the new record coming from the source table is not found then it will insert. if the changes are found on the table based on the lookup values, a new record is inserted.

Update: This is a conventional SCD-I approach . These attributes in the last dimension record version are updated. If we keep all the fields as Update then it performs SCD-I approach. If some of the fields are Insert and some are update then it applies SCD-II and the fields in which Update is applied will only update the data based upon the last version.

Punch Through: This mechanism is applied on those fields where the data changes very rarely and if it changes it is just a correction. For example, in case of names of the products or null values in price columns. It is also used for SCD_I but its slower than the update mechanism.

Note: If u mix Insert,update and punch through in one dimension, it works like hybrid slowly changing mechanism which is of type-6.

Date of last insert or update (without stream field as source) : adds and manges a Date field.

Date of last insert (without stream field as source) : adds and manges a Date fieldDate of last update (without stream field as source) : adds and manges a Date field.

Last version (without stream field as source) : adds and manges a Boolean field. (converted into Char(1) or boolean database data type depending on your database connection settings and availability of such data type). This acts as a current valid dimension entry entry indicator for the last version: So when a type II attribute changes and a new version is created (to keep track of the history) the ‘Last version’ attribute in the previous version is set to ‘False/N’ and the new record with the latest version is set to ‘True/Y’.

Note: This dimension entry is added automatically to the dimension table when the update is first run. If you have “NOT NULL” fields in your table, adding this empty row and then the entire step will fail! So make sure that you have a record with the ID field = 0 or 1 in your table if you don’t want PDI to insert a potentially invalid empty record.

This component will work faster if you apply the caching mechanism. A cache size of 0 caches as many rows as possible and until your JVM runs out of memory. Use this option wisely with dimensions that can’t grow too large. A cache size of -1 means that caching is disabled.

Note: There are various other components which can perform SCD in kettle. The Insert/update can also be used which performs SCD-II mechanism. If a new record is found then it will insert or else it will update the dimension. Similarly, the Update step performs SCD-I mechanism. However these components are very slow compared to dimension lookup/update. Similarly, you can use Merge Join(Diff) and then use Synchronise after merge which also performs SCD mechanism. This is fastest among all the above but however the number of records on both the dimension and staging should be same otherwise it wont work.



SCD Type 1 Implementation on Pentaho Data Integrator

This blog will talk about SCD Type 1 Implementation on Pentaho Data Integrator

Slowly Changing Dimension Type 1 does not preserve any historical versions of the data .
This methodology overwrites old data with new data, and therefore stores only the most current information. In this article let’s discuss the step by step implementation of SCD Type 1 using Pentaho.
The number of records we store in SCD Type 1 does not increase exponentially as this methodology overwrites old data with new data
Create table in Database for source and target and Create connections for database.

Table Input Step:

SCD Penthao

Drag and Drop the table input in spoon workspace and give the connection, then click on get select statement.

Then Click on ok.

Database Lookup:-

Drag And Drop The Database Lookup. Double Click on Database Lookup And Set The Following properties  and then click on ok.:-

SCD Penthao 2

Filter Rows:-

Set The following properties  of filter rows and click on ok.

SCD Penthao 3

Select Values:-

Before you work on select values, connect the table output and specify all the database field on to the output step.

SCD Penthao 4

Table Output :-

SCD Penthao 5


Set The Following On Update  and click on ok.

SCD Penthao 6

Program should Like the Following Figure :-

SCD Penthao 7


For any other query related to Pentaho Data Integrator, get in touch with us at Helical IT Solutions

Bidyut Kumar

Helical IT Solutions