DataStage provides various lookup types to select from. Learn more about each lookup type to optimize your data. We’ll cover Normal, Range, Sparse, and Case Less lookups. And you’ll understand why each type is useful. This article covers some of the most common lookup types. You can use whichever one works best for you! So, start building your own data pipeline! And, don’t forget to check out the rest of our articles for more tips and tricks!
There are two lookup types available in DataStage: the normal lookup type and the sparse lookup type. Normal lookup stores data in memory, while sparse looks up data directly from the database. It’s useful when the reference data set is too large to fit in memory. The sparse lookup can be used to achieve the same result. Listed below are the differences between the two types.
A normal lookup is an operation that uses the key columns of a table to perform a join operation. It also uses a hash table to find a key quickly in the virtual Data Set. The normal lookup type in DataStage will show as a composite operator in the score. There are several ways to implement a lookup. You can also use it to perform validation on rows. This type of operation is used when you need to compare values from two different tables, such as tables with the same primary key.
When working with a normal lookup stage, you must use the Expression Editor. This allows you to enter correct expressions. It is also very helpful in editing Lookup stages. You can use the editor to enter expressions for different fields, including the input and reference data. The editor also lets you specify if a data value meets a condition. Otherwise, the job will continue, fail, or drop it.
If a lookup fails, you can still choose the ‘Continue’ option to keep it going. If a record fails, the ‘Drop’ option will remove the record from the data set. Alternatively, you can use ‘Reject’ to send the records to a reject link. If you choose the Fail option, the job will move to an aborted state. If you use the Reject option, make sure you provide the link for a rejected record.
You might have heard of the normal and sparse DataStage lookup types. The former are used to process queries against a database table, and the latter perform such tasks in a more efficient manner. A normal lookup involves storing reference data in memory and performing the lookup in the database. A sparse lookup uses only one reference link instead of multiple. However, it is still important to use sparse lookup whenever possible.
A sparse DataStage lookup type refers to a data structure where the lookup table has only one reference link, no input or output links, and no rejects. The table contains values that approximate a mathematical function. The lookup operation uses this array of values to retrieve the output values from the database. This is a better option for large tables, outer joins, and joining multiple tables with the same keys.
The lookup stage has its own user interface. Instead of using a generic user interface, it presents a separate editor. When the lookup stage is edited, the left pane represents the input data while the right pane represents the lookup data. The Expression Editor helps you enter the correct expressions for the Lookup stage. You can select the lookup type to fit your data set. Then, you can select the lookup behavior you want to apply.
The DataStage lookup functions support the join, sort, and vector sort stages. The former searches one row or column and gets data from the same position in the other row or column. A connected lookup allows the user to define default values, while unconnected lookups do not. The LEFT JOIN command will return all rows in the left table, corresponding rows in the right table, and NULL on the right side.
The range lookup type will allow you to search records using a range and provides better performance. This type of lookup requires a column with an upper and lower bound and the appropriate operators. A funnel stage, on the other hand, copies multiple input data sets to a single output data set. It is similar to the normal lookup but saves the data directly in the database, and allows for the use of multiple sources.
A range lookup is a data integration method that compares a value of a source column with the values in a given range of other columns. You can define a range lookup on a stream or reference link. The resulting value is then displayed in the Sources and Lookups columns. Clicking on the Range lookup type will open the Range dialog box. The Expression box will appear.
The sparse lookup method requires a lookup stage and a reference link from an Oracle Connector stage. This method directly hits the database. This method is faster when a single input stream is used as a reference, but it is inefficient for large tables. The data read by these stages is loaded into memory in the same way as any other reference link. The disadvantages of using the sparse lookup method are that it is only suitable for parallel jobs and is not suited for real-time jobs.
Range lookup stage is the most suitable for large data sets that don’t require sorting. In addition, it doesn’t require sorting the reference columns. However, it can be slow, as it requires large blocks of shared memory. Large lookup tables can degrade performance when they become too large, and they require paging to avoid overflow. If you’re creating a data warehouse or a data mart, be sure to select the right range lookup type.
Range lookup stage is an enhanced version of earlier DataStage releases. It combines the features of a joiner and a lookup stage. The key columns in the lookup table are determined by the Lookup stage. The Lookup stage uses the values of the key columns and performs table lookups on the lookup tables. In addition to this, it supports a joiner, sorter, and drop condition.
Case Less lookup
If you’re new to DataStage, you’ve probably heard of the Sparse lookup type. This is a faster type of lookup because it directly hits the database. It is best used for reference data and input stream data, but is still slow when working with huge reference data. The left outer and right outer lookup types both work by transferring values from the left data set to the right data set.
You can also use a Condition Not Met option to check whether or not data meets a specified condition. If the condition check is not met, the job will move to an aborted state. The ‘Condition Not Met’ option will let you specify how you want the lookup job to behave when the data is not in the input table. The ‘Drop’ option simply drops the records.
Another type of lookup in DataStage is a Range Lookup. It requires additional preparation of the data for the lookup. The framework prepares this data automatically, but the performance hit is still high. You should consider the performance hit when using the Range Lookup type. But it is well worth the performance impact, if you don’t want to compromise on performance. However, this type of lookup is ideal for most applications.
When using the Case Less lookup type in DataStage, you need to remember that a join is a better option. The join type is faster because it requires less memory, but it is not as efficient when processing reference data. If you need to merge a few large datasets, you may want to consider the Join type. This is used when you need to merge data from multiple sources. Using the Joiner type is the best option if your reference data is very large.
A Case Less lookup is more efficient than a normal one. However, it is not recommended for normal equality matches, because it is slow. Use it if you need to fetch rows from reference data and if your input data is small compared to the reference data. You can also extract data sets to perform lookups on them. Then, use the DataStage wizard to create the jobs.