In SQL Server Integration Services, there are two types of look ups:
Normal look ups
Fuzzy look ups
What is the difference between them?
There are good descriptions of all of SSIS transformations on MSDN.
Lookup transformations perform lookups by joining data in input columns with columns in a reference dataset. You use the lookup to access additional information in a related table that is based on values in common columns.
As an example, if you are populating a fact table, you might need to use a lookup to get the surrogate key from a dimension table by joining based upon the business key.
Fuzzy Lookup transformations perform data cleaning tasks such as standardizing data, correcting data, and providing missing values. The Fuzzy Lookup transformation differs from the Lookup transformation in its use of fuzzy matching. The Lookup transformation uses an equi-join to locate matching records in the reference table. It returns records with at least one matching record, and returns records with no matching records. In contrast, the Fuzzy Lookup transformation uses fuzzy matching to return one or more close matches in the reference table.
Fuzzy lookups are commonly used to standardize addresses and names.
Related
I understand that the columnar databases put column data together on the disk rather than rows. I also understand that in traditional row-wise RDBMS, leaf index node of B-Tree contains pointer to the actual row.
But since columnar doesn't store rows together, and they are particularly designed for columnar operations, how do they differ in the indexing techniques?
Do they also use B-tress?
How do they index inside whatever datastructure they use?
Or there is no accepted format, every vendor have their own indexing scheme to cater their needs?
I have been searching, but unable to find any text. Every text I found is for row-wise DBMS.
There are no BTrees. (Or, if they are, they are not the main part of the design)
Infinidb stores 64K rows per chunk. Each column in that chunk is compressed and indexed. With the chunk is a list of things like min, max, avg, etc, for each column that may or may not help in queries.
Running a SELECT first looks at that summary info for each chunk to see if the WHERE clause might be satisfied by any of the rows in the chunk.
The chunks that pass that filtering get looked at in more detail.
There is no copy of a row. Instead, if, say, you ask for SELECT a,b,c, then the compressed info for 64K rows (in one chunk) for each of a, b, c need to be decompressed to further filter and deliver the row. So, it behooves you to list only the desired columns, not blindly say SELECT *.
Since every column is separately indexed all the time, there is no need to say INDEX(a). (I don't know if INDEX(a,b) can even be specified for a columnar DB.)
Caveat: I am describing Infinidb, which is available with MariaDB. I don't know about any other columnar engines.
If you understand
1)How columnar DBs store the data actually, and
2)How Indexes work, (how they store the data)
Then you may feel that there is no need of indexing in columnar Dbs.
For any kind of database rowid is very important, it is like the address where the data is stored.
Indexing is nothing but, mapping the rowids to the columns that are being indexed in a sorted order.
Columnar databases are born basing this logic. They try to store the data in this fashion itself, meaning - They store the data as a key-value pair in a serialized manner where the actual column value is Key and the rowid when the data is residing as its value and if they find any duplicates for a key, they just compress and store.
So if you compare the format how columnar databases store the data actually on Disk, it is almost the same (but not exactly because, as the difference is compression, representation of key-value in a vice versa fashion) how the row oriented databases store indexes.
That's the reason you don't need separate indexing again. and you won't find any columnar database trying to implement indexing.
Columnar Indexes (also known as "vertical data storage") stores data in a hash and compressed mode. All columns invoked in the index key are indexed separately. Hashing decrease the volume of data stored. The compressing method use only one value for repetitive occurrences (dictionnary, eventually partial).
This technic have two major difficulties :
First you can have collision, because a hash result can be the same
for two distinct values. So the index must manage collisions.
Second, the hash and compress algorithms used is a very heavy consumer of
resources like CPU.
Those type of indexes are stored as vectors.
Ordinary, those type of indexes are used only for read only tables, especially for the business intelligence (OLAP databases).
A columnar index can be used in a "seekable" way only for an equality predicate (COLUMN_A = OneValue). But it is also adequate for GROUPING or DISTINCT operations. Columnar index does not support range seek, including the LIKE 'foo%'.
Some database vendors have get around the huge resources needed while inserting or updating by adding some intermediate algorithms that decrease the CPU. This is the case for Microsoft SQL Server that use a delta store for newly modified rows. With this technic, the table can be used in a relational way like any classical OLTP dataabase.
For instance, Microsoft SQL Server introduced first the columnstore index in 2012 version, but this made the table read only. In 2014 the clustered columnstore index (all the columns of the table was indexed) was released and the table was writetable. And finally in the 2016 version, the columnstore index clustered ornot, no longer demands any part of the table to be read only.
This was made possible because a particular search algorithm, named "Batch Mode" was developed by Microsoft Research, and does not works by reading the data row by row...
To read :
Enhancements to SQL Server Column Stores
Columnstore and B+ tree –Are Hybrid Physical Designs Important?
I have a mysql database with 220 tables. The database is will structured but without any clear relations. I want to find a way to connect the primary key of each table to its correspondent foreign key.
I was thinking to write a script to discover the possible relation between two columns:
The content range should be similar in both of them
The foreign key name could be similar to the primary key table name
Those features are not sufficient to solve the problem. Do you have any idea how I could be more accurate and close to the solution. Also, If there's any available tool which do that.
Please Advice!
Sounds like you have a licensed app+RFS, and you want to save the data (which is an asset that belongs to the organisation), and ditch the app (due to the problems having exceeded the threshold of acceptability).
Happens all the time. Until something like this happens, people do not appreciate that their data is precious, that it out-lives any app, good or bad, in-house or third-party.
SQL Platform
If it was an honest SQL platform, it would have the SQL-compliant catalogue, and the catalogue would contain an entry for each reference. The catalogue is an entry-level SQL Compliance requirement. The code required to access the catalogue and extract the FOREIGN KEY declarations is simple, and it is written in SQL.
Unless you are saying "there are no Referential Integrity constraints, it is all controlled from the app layers", which means it is not a database, it is a data storage location, a Record Filing System, a slave of the app.
In that case, your data has no Referential Integrity
Pretend SQL Platform
Evidently non-compliant databases such as MySQL, PostgreSQL and Oracle fraudulently position themselves as "SQL", but they do not have basic SQL functionality, such as a catalogue. I suppose you get what you pay for.
Solution
For (a) such databases, such as your MySQL, and (b) data placed in an honest SQL container that has no FOREIGN KEY declarations, I would use one of two methods.
Solution 1
First preference.
use awk
load each table into an array
write scripts to:
determine the Keys (if your "keys" are ID fields, you are stuffed, details below)
determine any references between the Keys of the arrays
Solution 2
Now you could do all that in SQL, but then, the code would be horrendous, and SQL is not designed for that (table comparisons). Which is why I would use awk, in which case the code (for an experienced coder) is complex (given 220 files) but straight-forward. That is squarely within awks design and purpose. It would take far less development time.
I wouldn't attempt to provide code here, there are too many dependencies to identify, it would be premature and primitive.
Relational Keys
Relational Keys, as required by Codd's Relational Model, relate ("link", "map", "connect") each row in each table to the rows in any other table that it is related to, by Key. These Keys are natural Keys, and usually compound Keys. Keys are logical identifiers of the data. Thus, writing either awk programs or SQL code to determine:
the Keys
the occurrences of the Keys elsewhere
and thus the dependencies
is a pretty straight-forward matter, because the Keys are visible, recognisable as such.
This is also very important for data that is exported from the database to some other system (which is precisely what we are trying to do here). The Keys have meaning, to the organisation, and that meaning is beyond the database. Thus importation is easy. Codd wrote about this value specifically in the RM.
This is just one of the many scenarios where the value of Relational Keys, the absolute need for them, is appreciated.
Non-keys
Conversely, if your Record Filing System has no Relational Keys, then you are stuffed, and stuffed big time. The IDs are in fact record numbers in the files. They all have the same range, say 1 to 1 million. It is not reasonably possible to relate any given record number in one file to its occurrences in any other file, because record numbers have no meaning.
Record numbers are physical, they do not identify the data.
I see a record number 123456 being repeated in the Invoice file, now what other file does this relate to ? Every other possible file, Supplier, Customer, Part, Address, CreditCard, where it occurs once only, has a record number 123456 !
Whereas with Relational Keys:
I see IBM plus a sequence 1, 2, 3, ... in the Invoice table, now what other table does this relate to ? The only table that has IBM occurring once is the Customer table.
The moral of the story, to etch into one's mind, is this. Actually there are a few, even when limiting them to the context of this Question:
If you want a Relational Database, use Relational Keys, do not use Record IDs
If you want Referential Integrity, use Relational Keys, do not use Record IDs
If your data is precious, use Relational Keys, do not use Record IDs
If you want to export/import your data, use Relational Keys, do not use Record IDs
I have the following problem:
We have a lot of different, yet similar types of data items that we want to record in a (MariaDB) database. All data items have some common parameters such as id, username, status, file glob, type, comments, start & end time stamps. In addition there are many (let's say between 40 and 100) parameters that are specific to each type of data item.
We would prefer to have the different data item types in the same table because they will be displayed along with several other data, as they happen, in one single list in the web application. This will appear like an activity stream or "Facebook wall".
It seems that the normalised approach with a top-level generic table joined with specific tables underneath will lead to bad performance. We will have to do both a lot of joins and unions in order to display the activity stream, and the application will frequently poll with this query, so it's important that the query runs fast.
So, which is the better solution(s) in terms of performance and storage optimization?
to utilize MariaDB's dynamic columns
to just add in all the different kinds of columns we need in one table, and just accept that each data item type will only use a few of the columns, i.e. the rest will be null.
something else?
Does it matter if we use regular columns when a lot of the data in them will be null?
When should we use dynamic columns and when is it better to use regular columns?
I believe you should have separate columns for the values you are filtering by. However, you might have some unfiltered values. For those it might be a good idea to store them in a single column as a json object (simple to encode/decode).
A few columns -- the main ones for using in WHERE and ORDER BY clauses (but not necessarily all the columns you might filter on.
A JSON column or MariaDB Dynamic columns.
See my blog on why not to use EAV schema. I focus on how to do it in JSON, but MariaDB's Dynamic Columns is arguably better.
I have a couple of questions.
I would like to know if we need to worry about distribution in Netezza while using only select statements(not creating tables).
I am basically trying to create a dataset in SAS by connecting to Netezza and selecting the view which has a couple of joins. I am wondering how will this affect performance of Netezza if i am creating the table directly in SAS.
I am creating a table by joining another two tables on customer_id. However, the output dataset does not consist of customer_id as a column. Can i distribute this table on customer_id?
Thanks.
For your first question, you typically don't need to worry about distribution if you aren't creating a table. It does help to understand distribution methods for the tables you are selecting from, but it's certainly not a requirement. Having a distribution method that supports the particular joins you are doing can certainly help performance during the select (e.g. if your join columns are superset of the distribution columns then you'll get co-located joins), but if the target of the output is SAS, then there's no effect on the write of the dataset to SAS.
For your second question, a table is distributed either on a column, or columns, in the table itself, or via a RANDOM (aka round robin) distribution method. In your case, if you are storing your data set in a table on Netezza, you could not distribute the data on customer_id as that column is not included in the data set.
Which one is fast either Index or View both are used for optimization purpose both are implement on table's column so any one explain which one is more faster and what is difference between both of them and which scenario we use view and index.
VIEW
View is a logical table. It is a physical object which stores data logically. View just refers to data that is tored in base tables.
A view is a logical entity. It is a SQL statement stored in the database in the system tablespace. Data for a view is built in a table created by the database engine in the TEMP tablespace.
INDEX
Indexes are pointres that maps to the physical address of data. So by using indexes data manipulation becomes faster.
An index is a performance-tuning method of allowing faster retrieval of records. An index creates an entry for each value that appears in the indexed columns.
ANALOGY:
Suppose in a shop, assume you have multiple racks. Categorizing each rack based on the items saved is like creating an index. So, you would know where exactly to look for to find a particular item. This is indexing.
In the same shop, you want to know multiple data, say, the Products, inventory, Sales data and stuff as a consolidated report, then it can be compared to a view.
Hope this analogy explains when you have to use a view and when you have to use an index!
Both are different things in the perspective of SQL.
VIEWS
A view is nothing more than a SQL statement that is stored in the database with an associated name. A view is actually a composition of a table in the form of a predefined SQL query.
Views, which are kind of virtual tables, allow users to do the following:
A view can contain all rows of a table or select rows from a table. A view can be created from one or many tables which depends on the written SQL query to create a view.
Structure data in a way that users or classes of users find natural or intuitive.
Restrict access to the data such that a user can see and (sometimes) modify exactly what they need and no more.
Summarize data from various tables which can be used to generate reports.
INDEXES
While Indexes are special lookup tables that the database search engine can use to speed up data retrieval. Simply put, an index is a pointer to data in a table. An index in a database is very similar to an index in the back of a book.
For example, if you want to reference all pages in a book that discuss a certain topic, you first refer to the index, which lists all topics alphabetically and are then referred to one or more specific page numbers.
An index helps speed up SELECT queries and WHERE clauses, but it slows down data input, with UPDATE and INSERT statements. Indexes can be created or dropped with no effect on the data.
view:
1) view is also a one of the database object.
view contains logical data of a base table.where base table has actual data(physical data).another way we can say view is like a window through which data from table can be viewed or changed.
2) It is just simply a stored SQL statement with an object name. It can be used in any SELECT statement like a table.
index:
1) indexes will be created on columns.by using indexes the fetching of rows will be done quickly.
2) It is a way of cataloging the table-info based on 1 or more columns. One table may contain one/more indexes. Indexes are like a 2-D structure having ROWID & indexed-column (ordered). When a table-data is retrieved based on this column (col. which are used in WHERE clause), this index gets into the picture automatically and it's pointer search the required ROWIDs. These ROWIDs are now matched with actual table's ROWID and the records from table are shown.