In docs for various ORMs they always provide a way to create indexes, etc. They always mention to be sure to create the appropriate indexes for efficiency, as if that is inherent knowledge to a non-hand-written-SQLer who needs to use an ORM. My understanding of indexes (outside of PK) is basically: If you plan to do LIKE queries (ie, search) based on the contents of a column, you should use a full text index for that column. What else should I know regarding indexes (mostly pertaining to efficiency)? I feel like there is a world of knowledge at my door step, but there's a huge folded mouse pad jammed up under it, so I can't get through (I don't know why I felt like I needed to say that, but thanks for providing the couch).
Think of an index very roughly like the index in the back of a book. It's a totally separate area from the content of the book, where if you are seeking some specific value, you can go to the index and look it up (indexes are ordered, so finding things there is much quicker than scanning every page of the book).
The index entry has a page number, so you can then quickly go to the page seeking your topic. A database index is very similar; it is an ordered list of the relevant information in your database (the field(s) included in the index), with information for the database to find the records which match.
So... you would create an index when you have information that you need to search on frequently. Normal indexes don't help you for 'partial' seeks like LIKE queries, but any time you need to get a set of results where field X has certain value(s), they keep the DBMS from needing to 'scan' the whole table, looking for matching values.
They also help when you need to sort on a column.
Another thing to keep in mind; If the DBMS allows you to create single indexes that have multiple fields, be sure to investigate the effects of doing so, specific to your DBMS. An index that includes multiple fields is likely only to be fully (or at all) useful if all those fields are being used in a query. Conversely, having multiple indexes for a single table, with one field per index, may not be of much (or any) help for queries that are filtering/sorting by multiple fields.
You mentioned Full Text indexes and PKs (Primary Keys). These are different than regular indexes, though they often serve similar purposes.
First, note that a Primary Key is usually an index (in MSSQL, a 'Clustered Index', in fact), but this does not need to be the case specifically. As an example, an MSSQL PK is a Clustered Index by default; clustered indexes are special in that they are not a separate bit of data stored elsewhere, but the data itself is arranged in the table in order by the Clustered Index. This is why a popular PK is an int value that is auto-generated with sequential, increasing values. So, a Clustered Index sorts the data in the table specifically by the field's value. Compare this to a traditional dictionary; the entries themselves are ordered by the 'key', which is the word being defined.
But in MSSQL (check your DBMS documentation for your information), you can change the Clustered Index to be a different field, if you like. Sometimes this is done on datetime based fields.
Full Text indexes are different kinds of beasts entirely. They use some of the same principles, but what they are doing isn't exactly the same as normal indexes, which I am describing. Also: in some DBMS's, LIKE queries do not use the full text index; special query operators are required.
These indexes are different because their intent is not to find/sort on the whole value of the column (a number, a date, a short bit of char data), but instead to find individual words/phrases within the text field(s) being indexed.
They can also often enable searching for similar words, different tenses, common misspellings and the like, and typically ignore noise words. The different way in which they work is why they also may need different operators to use them. (again, check your local documentation for your DBMS!)
This answer is Oracle-specific, but the main points in the answers apply to most relational database systems
How to choose and optimize oracle indexes?
Related
We have a very large database and have been using shards which we want to get away from. The shards work by everytime a table gets really big, we start a new table that has the same schema as the previous table and keep a number in another table that helps us find which table the data is in. This is a cumbersome manual process and means we have data spread out over N different tables all with the same schema.
The idea we are trying for is to eliminate this need for sharding by using indexes. Our data lookup queries do not use unique keys and many records are returned that have the same values across columns.
The following illustrates many of our lookup selects for a particular table, the fields with the * indicate that field may or may not be in the select.
where clause: scheduled_test, *script, *label, *error_message
group/order: messenger_id, timeslice, script, label, error_message, step_sequence, *adapter_type
My thought is that I would not want to create an index with all of these 11 fields. I instead picked 3 of the ones that seemed to be used more commonly including the one that is always in the where clause. I had read that it is advisable not to have too wide an index with too many fields. I also had heard that the optimizer will use the indexed fields first and that it is not uncommon to have non unique indexes even though MSDN states to the effect that unique indexes is the big advantage. It's just not how our data is designed. I realize SQL will add something to the index to make it unique, but that doesn't seem to matter for our purposes.
When I look at the execution plan in sql server management studio on a query that is similar to what we might run, it says "clustered index seek cost 100%", but it is using the clustered index that I created so I am hoping this is better than the default clustered index that is just the generated primary key (previously how the table was defined). I am hoping that what I have here is as good or better than our sharding method and will eliminate the need for the shards.
We do insert alot of data into the tables all at once, but these rows all have the same data values across many columns and I think they would even tend to get inserted at the end as well. These inserts don't share values with older data and if the index is just 3 columns hopefully that would not be a very big hit on the inserts.
Does what I am saying seem reasonable or what else should I look into or consider ? Thanks alot, I am not that familiar with these types of indexing issues but have been looking on various websites and experimenting.
Generally, the narrower the clustered index the better as the clustering key of the clustered index will be added to all non-clustered indexes, making them less efficient.
SQL server will add a uniquifier to non-unique clustered indexes, making them (and all non-clustered indexes) even wider still.
If the space used by these indexes is not an issue for you, then you should consider whether the value of the clustered index key is ever increasing (or decreasing) as if it isn't, you will get page splits and fragmentation which will definitely hurt your inserts.
It's probably worth setting this up in a test system if you can to examine the impact different indexing strategies have on your normal queries.
So here I am looking at this huge oracle 10g table. I looked at its indexes and see that ALL of the columns are under one unique index. Does this provide actually provide any performance benefits?
Possibly, possibly not. It could be that the unique index is implementing a constraint to ensure that rows are indeed unique, and is not intended to help with performance at all. There could be a performance benefit for indexed lookup queries, because they won't need to access the actual table at all.
On the face of it it sounds like this should have been created as an INDEX ORGANIZED table.
From a performance standpoint, I would say that having all fields in a single index (unique or not) is generally not a good idea.
Any update to the table will result in that index being updated. In a "normal" table (the word normal used very loosely), there will be some fields not indexed. If one of those fields is updated, then it is more efficient because no indexes need to be updated.
The described index is somewhat limited in optimization of queries. For example, suppose the table has fields a, b, c, d, and e and that the index is defined with the fields in that order. Any query that does not reference a in the WHERE clause cannot use that index.
Depending on the number and size of fields involved, such an index could possibly have very large keys. With larger keys, it means that fewer keys can be stored in each page, and so an update to the index means that more page reads and writes will be involved.
Good day,
In SQL Server 2005, I have a table numerous columns, including a few boolean (bit) columns. For example,
table 'Person' has columns ID and columns HasItem1, HasItem2, HasItem3, HasItem4. This table is kinda large, so I would like to create indexes to get faster search results.
I know that is not I good idea to create an index on a bit column, so I thought about using a index with all of the bit columms. However, the thing is, all of these bit columns may or may not be in the query. Since the order of the indexed columns are important in an index, and that I don't know which ones will be used in the query, how should I handle this?
BTW, there is already clustered index that I can't remove.
I would suggest that this is probably not a good idea. Trying to index fields with very low cardinality will generally not make queries faster and you have the overhead of maintaining the index as well.
If you generally search for one of your bit fields with another field then a composite index on the two fields would probably benefit you.
If you were to create a composite index on the bit fields then this would help but only if the composite fields at the beginning of the index were provided. If you do not include the 1st value within the composite index then the index will probably not be used at all.
If, as an example bita was used in 90% of your queries and bitd in 70% and bits b and c in 20% then a composite index on (bita, bitd, bitb, bitc) would probably yield some benefit but for at least 10% of your queries and possibly even 40% the index would most likely not be used.
The best advice is probably to try it with the same data volumes and data cardinality and see what the Execution plan says.
I don't know a lot of specifics on sql server, but in general indexing a column that has non-unique data is not very effective. In some RDBMS systems, the optimizer will ignore indexes that are less than a certain percent unique anyway, so the index may as well not even exist.
Using a composite, or multi-column index can help, but only in particular cases where the filter constraints are in the same order that the index was built in. If you index includes 'field1, field2' and you are searching for 'field2, field1' or some other combination, the index may not be used. You could add an index for each of the particular search cases that you want to optimize, that is really all I can think of that you could do. And in the case that your data is not very unique, even after considering all of the bit fields, the index may be ignored anyway.
For example, if you have 3 bit fields, you are only segmenting your data into 8 distinct groups. If you have a reasonable number of rows in the table, segmenting it by 8 isn't going to be very effective.
Odds are it will be easier for SQL to query the large table with the person_id and item_id and BitValue then it will be to search a single table with Item1, Item2, ... ItemN.
I don't know about 2005 but in SQL Server 2000 (From Books Online):
"Columns of type bit cannot have indexes on them."
How about using checksum?
Add a int field named mysum to your table and execute this
UPDATE checksumtest SET mysum = CHECKSUM(hasitem1,hasitem2,hasitem3,hasitem4)
Now you have a value that represents the combination of bits.
Do the same checksum calc in your search query and match on mysum.
This may speed things up.
You should revisit the design of your database. Instead of having a table with fields HasItem1 to HasItem#, you should create a bridge entity, and a master Items table if you don't have one. The bridge entity (table), person_items, would have (a minimum of) two fields: person_id and item_id.
Designing the database this way doesn't lock you in to a database that only handles N number of items based on column definitions. You can add as many items as you want to a master Items table, and associate as many of them as you need with as many people as you need.
When working with tables in Oracle, how do you know when you are setting up a good index versus a bad index?
This depends on what you mean by 'good' and 'bad'. Basically you need to realise that every index you add will increase performance on any search by that column (so adding an index to the 'lastname' column of a person table will increase performance on queries that have "where lastname = " in them) but decrease write performance across the whole table.
The reason for this is when you add or update a row, it must add-to or update both the table itself and every index that row is a member of. So if you have five indexes on a table, each addition must write to six places - five indexes and the table - and an update may be touching up to six places in the worst case.
Index creation is a balancing act then between query speed and write speed. In some cases, such as a datamart that is only loaded with data once a week in an overnight job but queried thousands of times daily, it makes a great deal of sense to overload with indexes and speed the queries up as much as possible. In the case of online transaction processing systems however, you want to try and find a balance between them.
So in short, add indexes to columns that are used a lot in select queries, but try to avoid adding too many and so add the most-used columns first.
After that its a matter of load testing to see how the performance reacts under production conditions, and a lot of tweaking to find an aceeptable balance.
Fields that are diverse, highly specific, or unique make good indexes. Such as dates and timestamps, unique incrementing numbers (commonly used as primary keys), person's names, license plate numbers, etc...
A counterexample would be gender - there are only two common values, so the index doesn't really help reduce the number of rows that must be scanned.
Full-length descriptive free-form strings make poor indexes, as whoever is performing the query rarely knows the exact value of the string.
Linearly-ordered data (such as timestamps or dates) are commonly used as a clustered index, which forces the rows to be stored in index order, and allows in-order access, greatly speeding range queries (e.g. 'give me all the sales orders between October and December'). In such a case the DB engine can simply seek to the first record specified by the range and start reading sequentially until it hits the last one.
#Infamous Cow -- you must be thinking of primary keys, not indexes.
#Xenph Yan --
Something others have not touched on is choosing what kind of index to create. Some databases don't really give you much of a choice, but some have a large variety of possible indexes. B-trees are the default but not always the best kind of index. Choosing the right structure depends on the kind of usage you expect to have. What kind of queries do you need to support most? Are you in a read-mostly or write-mostly environment? Are your writes dominated by updates or appends? Etc, etc.
A description of the different types of indexes and their pros and cons is available here: http://20bits.com/2008/05/13/interview-questions-database-indexes/ .
Here's a great SQL Server article:
http://www.sql-server-performance.com/tips/optimizing_indexes_general_p1.aspx
Although the mechanics won't work on Oracle, the tips are very apropos (minus the thing on clustered indexes, which don't quite work the same way in Oracle).
Some rules of thumb if you are trying to improve a particular query.
For a particular table (where you think Oracle should start) try indexing each of the columns used in the WHERE clause. Put columns with equality first, followed by columns with a range or like.
For example:
WHERE CompanyCode = ? AND Amount BETWEEN 100 AND 200
If columns are very large in size (e.g. you are storing some XML or something) you may be better off leaving them out of the index. This will make the index smaller to scan, assuming you have to go to the table row to satisfy the select list anyway.
Alternatively, if all the values in the SELECT and WHERE clauses are in the index Oracle will not need to access the table row. So sometimes it is a good idea to put the selected values last in the index and avoid a table access all together.
You could write a book about the best ways to index - look for author Jonathan Lewis.
A good index is something that you can rely on to be unique for a specific table row.
One commonly used index scheme is the use of numbers which increment by 1 for each row in the table. Every row will end up having a different number index.
As a follow up to "What are indexes and how can I use them to optimise queries in my database?" where I am attempting to learn about indexes, what columns are good index candidates? Specifically for an MS SQL database?
After some googling, everything I have read suggests that columns that are generally increasing and unique make a good index (things like MySQL's auto_increment), I understand this, but I am using MS SQL and I am using GUIDs for primary keys, so it seems that indexes would not benefit GUID columns...
Indexes can play an important role in query optimization and searching the results speedily from tables. The most important step is to select which columns are to be indexed. There are two major places where we can consider indexing: columns referenced in the WHERE clause and columns used in JOIN clauses. In short, such columns should be indexed against which you are required to search particular records. Suppose, we have a table named buyers where the SELECT query uses indexes like below:
SELECT
buyer_id /* no need to index */
FROM buyers
WHERE first_name='Tariq' /* consider indexing */
AND last_name='Iqbal' /* consider indexing */
Since "buyer_id" is referenced in the SELECT portion, MySQL will not use it to limit the chosen rows. Hence, there is no great need to index it. The below is another example little different from the above one:
SELECT
buyers.buyer_id, /* no need to index */
country.name /* no need to index */
FROM buyers LEFT JOIN country
ON buyers.country_id=country.country_id /* consider indexing */
WHERE
first_name='Tariq' /* consider indexing */
AND
last_name='Iqbal' /* consider indexing */
According to the above queries first_name, last_name columns can be indexed as they are located in the WHERE clause. Also an additional field, country_id from country table, can be considered for indexing because it is in a JOIN clause. So indexing can be considered on every field in the WHERE clause or a JOIN clause.
The following list also offers a few tips that you should always keep in mind when intend to create indexes into your tables:
Only index those columns that are required in WHERE and ORDER BY clauses. Indexing columns in abundance will result in some disadvantages.
Try to take benefit of "index prefix" or "multi-columns index" feature of MySQL. If you create an index such as INDEX(first_name, last_name), don’t create INDEX(first_name). However, "index prefix" or "multi-columns index" is not recommended in all search cases.
Use the NOT NULL attribute for those columns in which you consider the indexing, so that NULL values will never be stored.
Use the --log-long-format option to log queries that aren’t using indexes. In this way, you can examine this log file and adjust your queries accordingly.
The EXPLAIN statement helps you to reveal that how MySQL will execute a query. It shows how and in what order tables are joined. This can be much useful for determining how to write optimized queries, and whether the columns are needed to be indexed.
Update (23 Feb'15):
Any index (good/bad) increases insert and update time.
Depending on your indexes (number of indexes and type), result is searched. If your search time is gonna increase because of index then that's bad index.
Likely in any book, "Index Page" could have chapter start page, topic page number starts, also sub topic page starts. Some clarification in Index page helps but more detailed index might confuse you or scare you. Indexes are also having memory.
Index selection should be wise. Keep in mind not all columns would require index.
Some folks answered a similar question here: How do you know what a good index is?
Basically, it really depends on how you will be querying your data. You want an index that quickly identifies a small subset of your dataset that is relevant to a query. If you never query by datestamp, you don't need an index on it, even if it's mostly unique. If all you do is get events that happened in a certain date range, you definitely want one. In most cases, an index on gender is pointless -- but if all you do is get stats about all males, and separately, about all females, it might be worth your while to create one. Figure out what your query patterns will be, and access to which parameter narrows the search space the most, and that's your best index.
Also consider the kind of index you make -- B-trees are good for most things and allow range queries, but hash indexes get you straight to the point (but don't allow ranges). Other types of indexes have other pros and cons.
Good luck!
It all depends on what queries you expect to ask about the tables. If you ask for all rows with a certain value for column X, you will have to do a full table scan if an index can't be used.
Indexes will be useful if:
The column or columns have a high degree of uniqueness
You frequently need to look for a certain value or range of values for
the column.
They will not be useful if:
You are selecting a large % (>10-20%) of the rows in the table
The additional space usage is an issue
You want to maximize insert performance. Every index on a table reduces insert and update performance because they must be updated each time the data changes.
Primary key columns are typically great for indexing because they are unique and are often used to lookup rows.
Any column that is going to be regularly used to extract data from the table should be indexed.
This includes:
foreign keys -
select * from tblOrder where status_id=:v_outstanding
descriptive fields -
select * from tblCust where Surname like "O'Brian%"
The columns do not need to be unique. In fact you can get really good performance from a binary index when searching for exceptions.
select * from tblOrder where paidYN='N'
In general (I don't use mssql so can't comment specifically), primary keys make good indexes. They are unique and must have a value specified. (Also, primary keys make such good indexes that they normally have an index created automatically.)
An index is effectively a copy of the column which has been sorted to allow binary search (which is much faster than linear search). Database systems may use various tricks to speed up search even more, particularly if the data is more complex than a simple number.
My suggestion would be to not use any indexes initially and profile your queries. If a particular query (such as searching for people by surname, for example) is run very often, try creating an index over the relevate attributes and profile again. If there is a noticeable speed-up on queries and a negligible slow-down on insertions and updates, keep the index.
(Apologies if I'm repeating stuff mentioned in your other question, I hadn't come across it previously.)
It really depends on your queries. For example, if you almost only write to a table then it is best not to have any indexes, they just slow down the writes and never get used. Any column you are using to join with another table is a good candidate for an index.
Also, read about the Missing Indexes feature. It monitors the actual queries being used against your database and can tell you what indexes would have improved the performace.
Your primary key should always be an index. (I'd be surprised if it weren't automatically indexed by MS SQL, in fact.) You should also index columns you SELECT or ORDER by frequently; their purpose is both quick lookup of a single value and faster sorting.
The only real danger in indexing too many columns is slowing down changes to rows in large tables, as the indexes all need updating too. If you're really not sure what to index, just time your slowest queries, look at what columns are being used most often, and index them. Then see how much faster they are.
Numeric data types which are ordered in ascending or descending order are good indexes for multiple reasons. First, numbers are generally faster to evaluate than strings (varchar, char, nvarchar, etc). Second, if your values aren't ordered, rows and/or pages may need to be shuffled about to update your index. That's additional overhead.
If you're using SQL Server 2005 and set on using uniqueidentifiers (guids), and do NOT need them to be of a random nature, check out the sequential uniqueidentifier type.
Lastly, if you're talking about clustered indexes, you're talking about the sort of the physical data. If you have a string as your clustered index, that could get ugly.
A GUID column is not the best candidate for indexing. Indexes are best suited to columns with a data type that can be given some meaningful order, ie sorted (integer, date etc).
It does not matter if the data in a column is generally increasing. If you create an index on the column, the index will create it's own data structure that will simply reference the actual items in your table without concern for stored order (a non-clustered index). Then for example a binary search can be performed over your index data structure to provide fast retrieval.
It is also possible to create a "clustered index" that will physically reorder your data. However you can only have one of these per table, whereas you can have multiple non-clustered indexes.
The ol' rule of thumb was columns that are used a lot in WHERE, ORDER BY, and GROUP BY clauses, or any that seemed to be used in joins frequently. Keep in mind I'm referring to indexes, NOT Primary Key
Not to give a 'vanilla-ish' answer, but it truly depends on how you are accessing the data
It should be even faster if you are using a GUID.
Suppose you have the records
100
200
3000
....
If you have an index(binary search, you can find the physical location of the record you are looking for in O( lg n) time, instead of searching sequentially O(n) time. This is because you dont know what records you have in you table.
Best index depends on the contents of the table and what you are trying to accomplish.
Taken an example A member database with a Primary Key of the Members Social Security Numnber. We choose the S.S. because the application priamry referes to the individual in this way but you also want to create a search function that will utilize the members first and last name. I would then suggest creating a index over those two fields.
You should first find out what data you will be querying and then make the determination of which data you need indexed.