I am doing a POC to ingest data from Oracle to Ignite cluster and Fetch the
data from Ignite in another application. When I created the Model and Cache,
I specified the Key as String and value as Custom Object. Data loaded to
cluster but then I querying "SELECT * FROM TB_USER" I am getting only two
column, i.e. _KEY and _VAL. I am trying to get all the column from the
TB_USER. What are the configuration required for this?
There are three ways of configuring SQL tables in Ignite:
DDL statements (create table). As far as I can see, you used something else.
QueryEntities. You should enlist all columns that you want to see in your table in the QueryEntity#fields property. All names should correspond to field names of your Java objects.
Annotations. Fields, that are annotated as #QuerySqlField will become columns in your table.
Related
The problem I'm trying to tackle is inserting and/or updating dynamic tables in a sink within an Azure Data Factory data flow. I've managed to get the source data, transform it how I want it and then send it to a sink. The pipeline ran successfully and it said it copied 37 rows (as expected) but investigation showed that no data was actually deposited in the target table. This was because the Table Action on the sink was set to 'None'. So in trying to fix this last part, it seems I don't have the 'Create' option but do have the 'Recreate' option (see screenshot of the sink below) which is not what I want as the datasource will eventually only have changed data. I need the process to create the table if it doesn't exist and then Upsert data. (Recreate drops the table and then creates it).
If I change the sink type from Inline to Dataset, then I can select Insert and Upsert, etc options but this is then not dynamic as I need to select a specific dataset.
So has anyone come across the same issue and have you managed to have dynamic sinks in your data flow where the table is created if it doesn't exist, then upsert data.
I guess I can add a Pre SQL script which takes care of the 'create the table if it doesn't exist' but I still can't select the Upsert option with inline tables.
For the CREATE TABLE IF NOT EXISTS issue, I would recommend a Stored Procedure that is executed in the pipeline prior to the Data Flow.
For Inline vs Dataset, you can make the Dataset very flexible:
So still based on your runtime table name and no schema, so no need to target a specific table.
For the UPSERT issue, make sure you have an AlterRow activity before the Sink:
I have about 100 tables to which we replicate data, e.g. from the Oracle database.
I would like to quickly check that the data replicated to the tables in db2 is the same as in the source system.
Does anyone have a way to do this? I can create 100 transformations, but that's monotonous and time consuming. I would prefer to process this in a loop.
I thought I would keep the queries in a table and reach into it for records.
I read the data from Table input (sql_db2, sql_source, table_name) and write do copy rows to result. Next I read single record and I read a single record and put it into a loop.
But here came a problem because I don't know how to dynamically compare the data for the tables. Each table has different columns and here I have a problem.
I don't know if this is also possible?
You can inject metadata (in this case your metadata would be the column and table names) to a lot of steps in Pentaho, you create a transformation to collect the metadata to inject to another transformation that has only the steps and some basic information, but the bulk of the information of the columns affected by the different steps is in the transformation injecting the metadata.
Check Pentaho official documentation about Metadata Injection (MDI) and the sample with a basic example of metadata injection available in your PDI installation.
I am trying to copy tables from one schema to another with the same Azure SQL db. So far, I have created a lookup pipeline and passed the parameters for the for each loop and copy activity. But my sink dataset is not taking the parameter value I have given under "table option" field rather it is taking the dummy table I chose when creating the sink dataset. Can someone tell how can I pass dynamic table name to a sink dataset?
I have given concat('dest_schema.STG_',#{item().table_name})} in the table option field.
To make the schema and table names dynamic, add Parameters to the Dataset:
Most important - do NOT import a schema. If you already have one defined in the Dataset, clear it. For this Dataset to be dynamic, you don't want improper schemas interfering with the process.
In the Copy activity, provide the values at runtime. These can be hardcoded, variables, parameters, or expressions, so very flexible.
If it's the same database, you can even use the same Dataset for both, just provide different values for the Source and Sink.
WARNING: If you use the "Auto-create table" option, the schema for the new table will define any character field as varchar(8000), which can cause serious performance problems.
MY OPINION:
While you can do this, one of my personal rules is to not cross the database boundary. If the Source and Sink are on the same SQL database, I would try to solve this problem with a Stored Procedure rather than a data factory.
The issue I am facing in my nodejs application is identical to this user's question: Cannot insert new value to BigQuery table after updating with new column using streaming API.
To my understanding changes such as widening a table's schema may require some period of time before streamed inserts can reference the new columns otherwise a 'no such field' error is returned. For me this error is not always consistent as sometimes I am able to successfully insert.
However, I specifically wanted to know if you could alternatively use a load job instead of streaming? If so what drawbacks does it have as I am not sure of the difference even having read the documentation.
Alternatively, if I do use streaming but with the ignoreUnknownValues option, does that mean that all of the data is eventually inserted including data referencing new columns? Just that new columns are not queryable until the table schema is finished updating?
I understand the difference between Internal tables and external tables in hive as below
1) if we drop the internal Table File and metadata will be deleted, however , in case of External only metadata will be
deleted
2) if the file data need to be shared by other tools/applications then we go for external table if not
internal table, so that if we drop the table(external) data will still be available for other tools/applications
I have gone through the answers for question "Difference between Hive internal tables and external tables? "
but still I am not clear about the proper uses cases for Internal Table
so my question is why is that I need to make an Internal table ? why cant I make everything as External table?
Use EXTERNAL tables when:
The data is also used outside of Hive.
For example, the data files are read and processed by an existing program that doesn't lock the files.
The data is permanent i.e used when needed.
Use INTERNAL tables when:
The data is temporary.
You want Hive to completely manage the lifecycle of the table and data.
Let's understand it with two simple scenarios:
Suppose you have a data set, and you have to perform some analytics/problem statements on it. Because of the nature of problem statements, few of them can be done by HiveQL, few of them need Pig Latin and few of them need Map Reduce etc., to get the job done. In this situation External Table comes into picture- the same data set can be used to solve entire analytics instead of having different different copies of same data set for the different different tools. Here Hive don't need authority on the data set because several tools are going to use it.
There can be a scenario, where entire analytics/problem statements can be solved by only HiveQL. In such situation Internal Table comes into picture- Means you can put the entire data set into Hive's Warehouse and Hive is going to have complete authority on the data set.