1. Merge in proper orderMany a time, data could be stored in several data tables, for example, product sales records of several categories, and employee profiles of each department. In this case, we need to merge the data from multiple data tables for combined use. For the several normal homogeneous TSeqs, you can use A.conj() or A.merge(x) to merge the records of each TSeq into RSeq for use. If the big data is used in the data table, then you can also use CS.conj@x() and CS.merge@x(x) to combine the data in each cursor of cursor sequence CS, and merge and read them out when retrieving.
The data in cursor can only be traversed once, so it is impossible to sort over again after merging and retrieving all data from cursor. In view of this, the data in each cursor must be ordered in case of merging the data from multiple cursors.
Next, let's learn about the usage and difference between CS.conj@x() and CS.merge@x(x). Firstly, let's have a look at situation about the simple union.
Four pieces of text data are respectively used to record the order information about wines, electrical appliances, foods, and books. In A6, the data in the four pieces of text data cursor will be united. To find out the order in which the data are retrieved, the following code retrieves 300 records each time, and suspends data retrieval once the retrieved data contains records of goods of different categories. In this case, the retrieved TSeq can be seen in B7 as follows:
As can be learned from the result, regarding the union cursor, after all wine order data are retrieved from the 1st text data table, start to retrieve the electrical appliance data from the 2nd text data table. In other words, after the simple union by using CS.conj@x() function, the records in the resulting cursor will be retrieved in the same order as each cursor is arranged in the cursor sequence CS.
In this case, we intend to have a clear view of the order in which the records are retrieved from cursor after merging in proper order. To server this purpose, only the first 300 entries are retrieved. The TSeq in B7 is shown below:
As can be seen, the data are retrieved in a specified order of Date. Once all wine order data of January 1stis retrieved, retrieving all electrical appliance order data of the January 1st will start. Because retrieving data with cursor is a forward-only operation that can only be performed from the first to the last, the order data in each cursor must be ordered by date. After using function CS.merge@x() to merge in proper order, by comparing the current computation expression value on each data table, the result cursor will choose from the cursors of sequence CS to retrieve data when retrieving records. In this way, we can ultimately get the result arranged in the specified order. In data retrieving, each cursor will still traverse the records in each data table for once.
When merging the data in multiple cursors in proper order, the multiple cursors are simply merged into one, and the orders in which to retrieve data in each cursor has adjusted, without increasing or decreasing any record data.
Before the data in cursor is merged in proper order by the product sequence number, you must ensure the data in each cursor is ordered for the product sequence number. To do so, in A5, use function cs.sortx() to complete the sorting.
Please note that the cursor and TSeq are sorted differently. Because there are usually great amount of data in the cursor, they cannot be loaded into the memory all at once for sorting. Therefore, the data retrieving is performed along with the data sorting. The data will be saved as temporary data files when they are accumulated to a certain amount. Once all data are retrieved and sorted, all temporary data files will be merged in proper order, and return as the result cursor.In B7, the retrieved records are shown below:
As can be seen, the ordered merging can be accomplished once the data in each cursor have been sorted.
When making the statistics, sometimes, you need to consolidate the data from multiple cursors, which is similar to joining the data from multiple tables. If the data in cursor are required to join a normal TSeq, then you can use cs.switch().
In A5~A8, perform the aggregate operations over the products in each category, and return the respective cursor of temporary files. In A9, the daily sales data for products in each category will be aligned and joined by date. From A10, retrieve the statistical results of the first 25 days, as shown below:
Once the cursor is aligned joined, a cursor will be returned. From which the retrieved result is similar to the TSeq joining, and all fields are composed of the records. Thus, when retrieving, you must note that the joined records take more memory than those in the normal cases. In addition, since data is composed of records while not values, please note the requirements on writing the expression, in particular the re-joining with the result cursor, when using the result cursor for computation.
From the joined cursor, filter out the total amount of food orders which are greater than the total amount of wine orders, and then generate the TSeq. In A12, return the first 100 rows of results:
In using the aligned joining with cursor, you must remember that the data in the cursor cannot be read into and maintain in the memory during retrieving the data. Instead, they can only be traversed for once from the first to the last. Therefore, regarding the join operation, the data in each cursor must be sorted, which is different from processing the multi-table join for database, and quite unlike the join() for normal TSeq. As shown in the above example, the data in A5~A8 are ordered by date, which can ensure the computation is correct when joining.
In order to explain this problem, we create a cursor using two in-memory TSeq. Let's have a look:
The TSeqs in A1and A2 are shown below:
In A5, you will see the aligned joining result:
The data in cursor is different to the normal TSeq. When looking for the New York state corresponding to the New York city for joining, the cursor of State data has already moved to the entry 32, and the previous records are unfindable for later computations. So, for most cities, the corresponding state is unfindable like this. Because the option @1 and @a are not used in function join@x() to specify the left join or full join, only cities finding out corresponding state are returned, and the data are quite few.
In A1, the data are sorted by STATEID:
In A6, you will see the joining result: