I'm learning indexing in PostgreSQL now. I started trying to create my index and analyzing how it will affect execution time. I created some tables with such columns:
also, I filled them with data. After that I created my custom index:
create index events_organizer_id_index on events(organizer_ID);
and executed this command (events table contains 148 rows):
explain analyse select * from events where events.organizer_ID = 4;
I was surprised that the search was executed without my index and I got this result:
As far as I know, if my index was used in search there would be the text like "Index scan on events".
So, can someone explain or give references to sites, please, how to use indexes effectively and where should I use them to see differences?
From "Rows removed by filter: 125" I see there are too few rows in the events table. Just add couple of thousands rows and give it another go
from the docs
Use real data for experimentation. Using test data for setting up indexes will tell you what indexes you need for the test data, but that is all.
It is especially fatal to use very small test data sets. While
selecting 1000 out of 100000 rows could be a candidate for an index,
selecting 1 out of 100 rows will hardly be, because the 100 rows probably fit within a single disk page, and there is no plan that can
beat sequentially fetching 1 disk page.
In most cases, when database using an index it gets only address where the row is located. It contains data block_id and the offset because there might be many rows in one block of 4 or 8 Kb.
So, the database first searches index for the block adress, then it looks for the block on disk, reads it and parses the line you need.
When there are too few rows they fit into one on in couple of data blocks which makes it easier and quicker for DB to read whole table without using index at all.
See it the following way:
The database decides which way is faster to find your tuple (=record) with organizer_id 4. There are two ways:
a) Read the index and then skip to the block which contains the data.
b) Read the heap and find the record there.
The information in your screenshot show 126 records (125 skipped + your record) with a length ("width") of 62 bytes. Including overhead these data fit into two database blocks of 8 KB. As a rotating disk or SSD reads a series of blocks anyway - they read always more blocks into the buffer - it's one read operation for these two blocks.
So the database decides that it is pointless to read first the index to find the correct record (of in our case two blocks) and then read the data from the heap with the information from the index. That would be two read operations. Even with modern technology newer than rotating disks this needs more time than just scanning the two blocks. That's why the database doesn't use the index.
Indexes on such small tables aren't good for searching. Nevertheless unique indexes avoid double entries.
Related
I have a table with 100 columns (yes, code smell and arguably a potentially less optimized design). The table has an 'id' as PK. No other column is indexed.
So, if I fire a query like:
SELECT first_name from EMP where id = 10
Will SQL Server (or any other RDBMS) have to load the entire row (all columns) in memory and then return only the first_name?
(In other words - the page that contains the row id = 10 if it isn't in the memory already)
I think the answer is yes! unless it has column markers within a row. I understand there might be optimization techniques, but is it a default behavior?
[EDIT]
After reading some of your comments, I realized I asked an XY question unintentionally. Basically, we have tables with 100s of millions of rows with 100 columns each and receive all sorts of SELECT queries on them. The WHERE clause also changes but no incoming request needs all columns. Many of those cell values are also NULL.
So, I was thinking of exploring a column-oriented database to achieve better compression and faster retrieval. My understanding is that column-oriented databases will load only the requested columns. Yes! Compression will help too to save space and hopefully performance as well.
For MySQL: Indexes and data are stored in "blocks" of 16KB. Each level of the B+Tree holding the PRIMARY KEY in your case needs to be accessed. For example a million rows, that is 3 blocks. Within the leaf block, there are probably dozens of rows, with all their columns (unless a column is "too big"; but that is a different discussion).
For MariaDB's Columnstore: The contents of one columns for 64K rows is held in a packed, compressed structure that varies in size and structure. Before getting to that, the clump of 64K rows must be located. After getting it, it must be unpacked.
In both cases, the structure of the data on disk is a compromises between speed and space for both simple and complex queries.
Your simple query is easy and efficient to doing a regular RDBMS, but messier to do in a Columnstore. Columnstore is a niche market in which your query is abnormal.
Be aware that fetching blocks are typically the slowest part of performing the query, especially when I/O is required. There is a cache of blocks in RAM.
Say I have a database that holds information about books and their dates of publishing. (two attributes, bookName and publicationDate).
Say that the attribute publicationDate has a Hash Index.
If I wanted to display every book that was published in 2010 I would enter this query : select bookName from Books where publicationDate=2010.
In my lecture, it is explained that if there is a big volume of data and that the publication dates are very diverse, the more optimized way is to use the Hash index in order to keep only the books published in 2010.
However, if the vast majority of the books that are in the database were published in 2010 it is better to search the database sequentially in terms of performance.
I really don't understand why? What are the situations where using an index is more optimized and why?
It is surprising that you are learning about hash indexes without understanding this concept. Hash indexing is a pretty advanced database concept; most databases don't even support them.
Although the example is quite misleading. 2010 is not a DATE; it is a YEAR. This is important because a hash index only works on equality comparisons. So the natural way to get a year of data from dates:
where publicationDate >= date '2010-01-01' and
publicationDate < date '2011-01-01'
could not use a hash index because the comparisons are not equality comparisons.
Indexes can be used for several purposes:
To quickly determine which rows match filtering conditions so fewer data pages need to be read.
To identify rows with common key values for aggregations.
To match rows between tables for joins.
To support unique constraints (via unique indexes).
And for b-tree indexes, to support order by.
This is the first purpose, which is to reduce the number of data pages being read. Reading a data page is non-trivial work, because it needs to be fetched from disk. A sequential scan reads all data pages, regardless of whether or not they are needed.
If only one row matches the index conditions, then only one page needs to be read. That is a big win on performance. However, if every page has a row that matches the condition, then you are reading all the pages anyway. The index seems less useful.
And using an index is not free. The index itself needs to be loaded into memory. The keys need to be hashed and processed during the lookup operation. All of this overhead is unnecessary if you just scan the pages (although there is other overhead for the key comparisons for filtering).
Using an index has a performance cost. If the percentage of matches is a small fraction of the whole table, this cost is more than made up for by not having to scan the whole table. But if there's a large percentage of matches, it's faster to simply read the table.
There is the cost of reading the index. A small, frequently used index might be in memory, but a large or infrequently used one might be on disk. That means slow disk access to search the index and get the matching row numbers. If the query matches a small number of rows this overhead is a win over searching the whole table. If the query matches a large number of rows, this overhead is a waste; you're going to have to read the whole table anyway.
Then there is an IO cost. With disks it's much, much faster to read and write sequentially than randomly. We're talking 10 to 100 times faster.
A spinning disk has a physical part, the head, it must move around to read different parts of the disk. The time it takes to move is known as "seek time". When you skip around between rows in a table, possibly out of order, this is random access and induces seek time. In contrast, reading the whole table is likely to be one long continuous read; the head does not have to jump around, there is no seek time.
SSDs are much, much faster, there's no physical parts to move, but they're still much faster for sequential access than random.
In addition, random access has more overhead between the operating system and the disk; it requires more instructions.
So if the database decides a query is going to match most of the rows of a table, it can decide that it's faster to read them sequentially and weed out the non-matches, than to look up rows via the index and using slower random access.
Consider a bank of post office boxes, each numbered in a big grid. It's pretty fast to look up each box by number, but it's much faster to start at a box and open them in sequence. And we have an index of who owns which box and where they live.
You need to get the mail for South Northport. You look up in the index which boxes belong to someone from South Northport, see there's only a few of them, and grab the mail individually. That's an indexed query and random access. It's fast because there's only a few mailboxes to check.
Now I ask you to get the mail for everyone but South Northport. You could use the index in reverse: get the list of boxes for South Northport, subtract those from the list of every box, and then individually get the mail for each box. But this would be slow, random access. Instead, since you're going to have to open nearly every box anyway, it is faster to check every box in sequence and see if it's mail for South Northport.
More formally, the indexed vs table scan performance is something like this.
# Indexed query
C[index] + (C[random] * M)
# Full table scan
(C[sequential] + C[match]) * N
Where C are various constant costs (or near enough constant), M is the number of matching rows, and N is the number of rows in the table.
We know C[sequential] is 10 to 100 times faster than C[random]. Because disk access is so much slower than CPU or memory operations, C[match] (the cost of checking if a row matches) will be relatively small compared to C[sequential]. More formally...
C[random] >> C[sequential] >> C[match]
Using that we can assume that C[sequential] + C[match] is C[sequential].
# Indexed query
C[index] + (C[random] * M)
# Full table scan
C[sequential] * N
When M << N the indexed query wins. As M approaches N, the full table scan wins.
Note that the cost of using the index isn't really constant. C[index] is things like loading the index, looking up a key, and reading the row IDs. This can be quite variable depending on the size of the index, type of index, and whether it is on disk (cold) or in memory (hot). This is why the first few queries are often rather slow when you've first started a database server.
In the real world it's more complicated than that. In reality rows are broken up into data pages and databases have many tricks to optimize queries and disk access. But, generally, if you're matching most of the rows a full table scan will beat an indexed lookup.
Hash indexes are of limited use these days. It is a simple key/value pair and can only be used for equality checks. Most databases use a B-Tree as their standard index. They're a little more costly, but can handle a broader range of operations including equality, ranges, comparisons, and prefix searches such as like 'foo%'.
The Postgres Index Types documentation is pretty good high level run-down of the various advantages and disadvantages of types of indexes.
There was a query in production which was running for several hours(5-6) hours. I looked into its execution plan, and found that it was ignoring a parallel hint on a huge table. Reason - it was using TABLE ACCESS BY INDEX ROWID. So after I added a /*+ full(huge_table) */ hint before the parallel(huge_table) hint, the query started running in parallel, and it finished in less than 3 minutes. What I could not fathom was the reason for this HUGE difference in performance.
The following are the advantages of parallel FTS I can think of:
Parallel operations are inherently fast if you have more idle CPUs.
Parallel operations in 10g are direct I/O which bypass
buffer cache which means there is no risk of "buffer busy waits" or
any other contention for buffer cache.
Sure there are the above advantages but then again the following disadvantages are still there:
Parallel operations still have to do I/O, and this I/O would be more than what we have for TABLE ACCESS BY INDEX ROWID as the entire table is scanned and is costlier(all physical reads)
Parallel operations are not very scalable which means if there aren't enough free resources, it is going to be slow
With the above knowledge at hand, I see only one reason that could have caused the poor performance for the query when it used ACCESS BY INDEX ROWID - some sort of contention like "busy buffer waits". But it doesn't show up on the AWR top 5 wait events. The top two events were "db file sequential read" and "db file scattered read". Is there something else that I have missed to take into consideration? Please enlighten me.
First, without knowing anything about your data volumes, statistics, the selectivity of your predicates, etc. I would guess that the major benefit you're seeing is from doing a table scan rather than trying to use an index. Indexes are not necessarily fast and table scans are not necessarily slow. If you are using a rowid from an index to access a row, Oracle is limited to doing single block reads (sequential reads in Oracle terms) and that it's going to have to read the same block many times if the block has many rows of interest. A full table scan, on the other hand, can do nice, efficient multiblock reads (scattered reads in Oracle terms). Sure, an individual single block read is going to be more efficient than a single multiblock read but the multiblock read is much more efficient per byte read. Additionally, if you're using an index, you've potentially got to read a number of blocks from the index periodically to find out the next rowid to read from the table.
You don't actually need to read all that much data from the table before a table scan is more efficient than an index. Depending on a host of other factors, the tipping point is probably in the 10-20% range (that's a very, very rough guess). Imagine that you had to get a bunch of names from the phone book and that the phone book had an index that included the information you're filtering on and the page that the entry is on. You could use an index to find the name of a single person you want to look at, flip to the indicated page, record the information, flip back to the index, find the next name, flip back, etc. Or you could simply start at the first name, scan until you find a name of interest, record the information, and continue the scan. It doesn't take too long before you're better off ignoring the index and just reading from the table.
Adding parallelism doesn't reduce the amount of work your query does (in fact, adding in parallel query coordination means that you're doing more work). It's just that you're doing that work over a shorter period of elapsed time by using more of the server's available resources. If you're running the query with 6 parallel slaves, that could certainly allow the query to run 5 times faster overall (parallel query obviously scales a bit less than linearly because of overheads). If that's the case, you'd expect that doing a table scan made the query 20 times faster and adding parallelism added another factor of 5 to get your 100x improvement.
How would you tackle the following storage and retrieval problem?
Roughly 2.000.000 rows will be added each day (365 days/year) with the following information per row:
id (unique row identifier)
entity_id (takes on values between 1 and 2.000.000 inclusive)
date_id (incremented with one each day - will take on values between 1 and 3.650 (ten years: 1*365*10))
value_1 (takes on values between 1 and 1.000.000 inclusive)
value_2 (takes on values between 1 and 1.000.000 inclusive)
entity_id combined with date_id is unique. Hence, at most one row per entity and date can be added to the table. The database must be able to hold 10 years worth of daily data (7.300.000.000 rows (3.650*2.000.000)).
What is described above is the write patterns. The read pattern is simple: all queries will be made on a specific entity_id. I.e. retrieve all rows describing entity_id = 12345.
Transactional support is not needed, but the storage solution must be open-sourced. Ideally I'd like to use MySQL, but I'm open for suggestions.
Now - how would you tackle the described problem?
Update: I was asked to elaborate regarding the read and write patterns. Writes to the table will be done in one batch per day where the new 2M entries will be added in one go. Reads will be done continuously with one read every second.
"Now - how would you tackle the described problem?"
With simple flat files.
Here's why
"all queries will be made on a
specific entity_id. I.e. retrieve all
rows describing entity_id = 12345."
You have 2.000.000 entities. Partition based on entity number:
level1= entity/10000
level2= (entity/100)%100
level3= entity%100
The each file of data is level1/level2/level3/batch_of_data
You can then read all of the files in a given part of the directory to return samples for processing.
If someone wants a relational database, then load files for a given entity_id into a database for their use.
Edit On day numbers.
The date_id/entity_id uniqueness rule is not something that has to be handled. It's (a) trivially imposed on the file names and (b) irrelevant for querying.
The date_id "rollover" doesn't mean anything -- there's no query, so there's no need to rename anything. The date_id should simply grow without bound from the epoch date. If you want to purge old data, then delete the old files.
Since no query relies on date_id, nothing ever needs to be done with it. It can be the file name for all that it matters.
To include the date_id in the result set, write it in the file with the other four attributes that are in each row of the file.
Edit on open/close
For writing, you have to leave the file(s) open. You do periodic flushes (or close/reopen) to assure that stuff really is going to disk.
You have two choices for the architecture of your writer.
Have a single "writer" process that consolidates the data from the various source(s). This is helpful if queries are relatively frequent. You pay for merging the data at write time.
Have several files open concurrently for writing. When querying, merge these files into a single result. This is helpful is queries are relatively rare. You pay for merging the data at query time.
Use partitioning. With your read pattern you'd want to partition by entity_id hash.
You might want to look at these questions:
Large primary key: 1+ billion rows MySQL + InnoDB?
Large MySQL tables
Personally, I'd also think about calculating your row width to give you an idea of how big your table will be (as per the partitioning note in the first link).
HTH.,
S
Your application appears to have the same characteristics as mine. I wrote a MySQL custom storage engine to efficiently solve the problem. It is described here
Imagine your data is laid out on disk as an array of 2M fixed length entries (one per entity) each containing 3650 rows (one per day) of 20 bytes (the row for one entity per day).
Your read pattern reads one entity. It is contiguous on disk so it takes 1 seek (about 8mllisecs) and read 3650x20 = about 80K at maybe 100MB/sec ... so it is done in a fraction of a second, easily meeting your 1-query-per-second read pattern.
The update has to write 20 bytes in 2M different places on disk. IN simplest case this would take 2M seeks each of which takes about 8millisecs, so it would take 2M*8ms = 4.5 hours. If you spread the data across 4 “raid0” disks it could take 1.125 hours.
However the places are only 80K apart. In the which means there are 200 such places within a 16MB block (typical disk cache size) so it could operate at anything up to 200 times faster. (1 minute) Reality is somewhere between the two.
My storage engine operates on that kind of philosophy, although it is a little more general purpose than a fixed length array.
You could code exactly what I have described. Putting the code into a MySQL pluggable storage engine means that you can use MySQL to query the data with various report generators etc.
By the way, you could eliminate the date and entity id from the stored row (because they are the array indexes) and may be the unique id – if you don't really need it since (entity id, date) is unique, and store the 2 values as 3-byte int. Then your stored row is 6 bytes, and you have 700 updates per 16M and therefore a faster inserts and a smaller file.
Edit Compare to Flat Files
I notice that comments general favor flat files. Don't forget that directories are just indexes implemented by the file system and they are generally optimized for relatively small numbers of relatively large items. Access to files is generally optimized so that it expects a relatively small number of files to be open, and has a relatively high overhead for open and close, and for each file that is open. All of those "relatively" are relative to the typical use of a database.
Using file system names as an index for a entity-Id which I take to be a non-sparse integer 1 to 2Million is counter-intuitive. In a programming you would use an array, not a hash-table, for example, and you are inevitably going to incur a great deal of overhead for an expensive access path that could simply be an array indeing operation.
Therefore if you use flat files, why not use just one flat file and index it?
Edit on performance
The performance of this application is going to be dominated by disk seek times. The calculations I did above determine the best you can do (although you can make INSERT quicker by slowing down SELECT - you can't make them both better). It doesn't matter whether you use a database, flat-files, or one flat-file, except that you can add more seeks that you don't really need and slow it down further. For example, indexing (whether its the file system index or a database index) causes extra I/Os compared to "an array look up", and these will slow you down.
Edit on benchmark measurements
I have a table that looks very much like yours (or almost exactly like one of your partitions). It was 64K entities not 2M (1/32 of yours), and 2788 'days'. The table was created in the same INSERT order that yours will be, and has the same index (entity_id,day). A SELECT on one entity takes 20.3 seconds to inspect the 2788 days, which is about 130 seeks per second as expected (on 8 millisec average seek time disks). The SELECT time is going to be proportional to the number of days, and not much dependent on the number of entities. (It will be faster on disks with faster seek times. I'm using a pair of SATA2s in RAID0 but that isn't making much difference).
If you re-order the table into entity order
ALTER TABLE x ORDER BY (ENTITY,DAY)
Then the same SELECT takes 198 millisecs (because it is reading the order entity in a single disk access).
However the ALTER TABLE operation took 13.98 DAYS to complete (for 182M rows).
There's a few other things the measurements tell you
1. Your index file is going to be as big as your data file. It is 3GB for this sample table. That means (on my system) all the index at disk speeds not memory speeds.
2.Your INSERT rate will decline logarithmically. The INSERT into the data file is linear but the insert of the key into the index is log. At 180M records I was getting 153 INSERTs per second, which is also very close to the seek rate. It shows that MySQL is updating a leaf index block for almost every INSERT (as you would expect because it is indexed on entity but inserted in day order.). So you are looking at 2M/153 secs= 3.6hrs to do your daily insert of 2M rows. (Divided by whatever effect you can get by partition across systems or disks).
I had similar problem (although with much bigger scale - about your yearly usage every day)
Using one big table got me screeching to a halt - you can pull a few months but I guess you'll eventually partition it.
Don't forget to index the table, or else you'll be messing with tiny trickle of data every query; oh, and if you want to do mass queries, use flat files
Your description of the read patterns is not sufficient. You'll need to describe what amounts of data will be retrieved, how often and how much deviation there will be in the queries.
This will allow you to consider doing compression on some of the columns.
Also consider archiving and partitioning.
If you want to handle huge data with millions of rows it can be considered similar to time series database which logs the time and saves the data to the database. Some of the ways to store the data is using InfluxDB and MongoDB.
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Given that indexing is so important as your data set increases in size, can someone explain how indexing works at a database-agnostic level?
For information on queries to index a field, check out How do I index a database column.
Why is it needed?
When data is stored on disk-based storage devices, it is stored as blocks of data. These blocks are accessed in their entirety, making them the atomic disk access operation. Disk blocks are structured in much the same way as linked lists; both contain a section for data, a pointer to the location of the next node (or block), and both need not be stored contiguously.
Due to the fact that a number of records can only be sorted on one field, we can state that searching on a field that isn’t sorted requires a Linear Search which requires (N+1)/2 block accesses (on average), where N is the number of blocks that the table spans. If that field is a non-key field (i.e. doesn’t contain unique entries) then the entire tablespace must be searched at N block accesses.
Whereas with a sorted field, a Binary Search may be used, which has log2 N block accesses. Also since the data is sorted given a non-key field, the rest of the table doesn’t need to be searched for duplicate values, once a higher value is found. Thus the performance increase is substantial.
What is indexing?
Indexing is a way of sorting a number of records on multiple fields. Creating an index on a field in a table creates another data structure which holds the field value, and a pointer to the record it relates to. This index structure is then sorted, allowing Binary Searches to be performed on it.
The downside to indexing is that these indices require additional space on the disk since the indices are stored together in a table using the MyISAM engine, this file can quickly reach the size limits of the underlying file system if many fields within the same table are indexed.
How does it work?
Firstly, let’s outline a sample database table schema;
Field name Data type Size on disk
id (Primary key) Unsigned INT 4 bytes
firstName Char(50) 50 bytes
lastName Char(50) 50 bytes
emailAddress Char(100) 100 bytes
Note: char was used in place of varchar to allow for an accurate size on disk value.
This sample database contains five million rows and is unindexed. The performance of several queries will now be analyzed. These are a query using the id (a sorted key field) and one using the firstName (a non-key unsorted field).
Example 1 - sorted vs unsorted fields
Given our sample database of r = 5,000,000 records of a fixed size giving a record length of R = 204 bytes and they are stored in a table using the MyISAM engine which is using the default block size B = 1,024 bytes. The blocking factor of the table would be bfr = (B/R) = 1024/204 = 5 records per disk block. The total number of blocks required to hold the table is N = (r/bfr) = 5000000/5 = 1,000,000 blocks.
A linear search on the id field would require an average of N/2 = 500,000 block accesses to find a value, given that the id field is a key field. But since the id field is also sorted, a binary search can be conducted requiring an average of log2 1000000 = 19.93 = 20 block accesses. Instantly we can see this is a drastic improvement.
Now the firstName field is neither sorted nor a key field, so a binary search is impossible, nor are the values unique, and thus the table will require searching to the end for an exact N = 1,000,000 block accesses. It is this situation that indexing aims to correct.
Given that an index record contains only the indexed field and a pointer to the original record, it stands to reason that it will be smaller than the multi-field record that it points to. So the index itself requires fewer disk blocks than the original table, which therefore requires fewer block accesses to iterate through. The schema for an index on the firstName field is outlined below;
Field name Data type Size on disk
firstName Char(50) 50 bytes
(record pointer) Special 4 bytes
Note: Pointers in MySQL are 2, 3, 4 or 5 bytes in length depending on the size of the table.
Example 2 - indexing
Given our sample database of r = 5,000,000 records with an index record length of R = 54 bytes and using the default block size B = 1,024 bytes. The blocking factor of the index would be bfr = (B/R) = 1024/54 = 18 records per disk block. The total number of blocks required to hold the index is N = (r/bfr) = 5000000/18 = 277,778 blocks.
Now a search using the firstName field can utilize the index to increase performance. This allows for a binary search of the index with an average of log2 277778 = 18.08 = 19 block accesses. To find the address of the actual record, which requires a further block access to read, bringing the total to 19 + 1 = 20 block accesses, a far cry from the 1,000,000 block accesses required to find a firstName match in the non-indexed table.
When should it be used?
Given that creating an index requires additional disk space (277,778 blocks extra from the above example, a ~28% increase), and that too many indices can cause issues arising from the file systems size limits, careful thought must be used to select the correct fields to index.
Since indices are only used to speed up the searching for a matching field within the records, it stands to reason that indexing fields used only for output would be simply a waste of disk space and processing time when doing an insert or delete operation, and thus should be avoided. Also given the nature of a binary search, the cardinality or uniqueness of the data is important. Indexing on a field with a cardinality of 2 would split the data in half, whereas a cardinality of 1,000 would return approximately 1,000 records. With such a low cardinality the effectiveness is reduced to a linear sort, and the query optimizer will avoid using the index if the cardinality is less than 30% of the record number, effectively making the index a waste of space.
Classic example "Index in Books"
Consider a "Book" of 1000 pages, divided by 10 Chapters, each section with 100 pages.
Simple, huh?
Now, imagine you want to find a particular Chapter that contains a word "Alchemist". Without an index page, you have no other option than scanning through the entire book/Chapters. i.e: 1000 pages.
This analogy is known as "Full Table Scan" in database world.
But with an index page, you know where to go! And more, to lookup any particular Chapter that matters, you just need to look over the index page, again and again, every time. After finding the matching index you can efficiently jump to that chapter by skipping the rest.
But then, in addition to actual 1000 pages, you will need another ~10 pages to show the indices, so totally 1010 pages.
Thus, the index is a separate section that stores values of indexed
column + pointer to the indexed row in a sorted order for efficient
look-ups.
Things are simple in schools, isn't it? :P
An index is just a data structure that makes the searching faster for a specific column in a database. This structure is usually a b-tree or a hash table but it can be any other logic structure.
The first time I read this it was very helpful to me. Thank you.
Since then I gained some insight about the downside of creating indexes:
if you write into a table (UPDATE or INSERT) with one index, you have actually two writing operations in the file system. One for the table data and another one for the index data (and the resorting of it (and - if clustered - the resorting of the table data)). If table and index are located on the same hard disk this costs more time. Thus a table without an index (a heap) , would allow for quicker write operations. (if you had two indexes you would end up with three write operations, and so on)
However, defining two different locations on two different hard disks for index data and table data can decrease/eliminate the problem of increased cost of time. This requires definition of additional file groups with according files on the desired hard disks and definition of table/index location as desired.
Another problem with indexes is their fragmentation over time as data is inserted. REORGANIZE helps, you must write routines to have it done.
In certain scenarios a heap is more helpful than a table with indexes,
e.g:- If you have lots of rivalling writes but only one nightly read outside business hours for reporting.
Also, a differentiation between clustered and non-clustered indexes is rather important.
Helped me:- What do Clustered and Non clustered index actually mean?
Now, let’s say that we want to run a query to find all the details of any employees who are named ‘Abc’?
SELECT * FROM Employee
WHERE Employee_Name = 'Abc'
What would happen without an index?
Database software would literally have to look at every single row in the Employee table to see if the Employee_Name for that row is ‘Abc’. And, because we want every row with the name ‘Abc’ inside it, we can not just stop looking once we find just one row with the name ‘Abc’, because there could be other rows with the name Abc. So, every row up until the last row must be searched – which means thousands of rows in this scenario will have to be examined by the database to find the rows with the name ‘Abc’. This is what is called a full table scan
How a database index can help performance
The whole point of having an index is to speed up search queries by essentially cutting down the number of records/rows in a table that need to be examined. An index is a data structure (most commonly a B- tree) that stores the values for a specific column in a table.
How does B-trees index work?
The reason B- trees are the most popular data structure for indexes is due to the fact that they are time efficient – because look-ups, deletions, and insertions can all be done in logarithmic time. And, another major reason B- trees are more commonly used is because the data that is stored inside the B- tree can be sorted. The RDBMS typically determines which data structure is actually used for an index. But, in some scenarios with certain RDBMS’s, you can actually specify which data structure you want your database to use when you create the index itself.
How does a hash table index work?
The reason hash indexes are used is because hash tables are extremely efficient when it comes to just looking up values. So, queries that compare for equality to a string can retrieve values very fast if they use a hash index.
For instance, the query we discussed earlier could benefit from a hash index created on the Employee_Name column. The way a hash index would work is that the column value will be the key into the hash table and the actual value mapped to that key would just be a pointer to the row data in the table. Since a hash table is basically an associative array, a typical entry would look something like “Abc => 0x28939″, where 0x28939 is a reference to the table row where Abc is stored in memory. Looking up a value like “Abc” in a hash table index and getting back a reference to the row in memory is obviously a lot faster than scanning the table to find all the rows with a value of “Abc” in the Employee_Name column.
The disadvantages of a hash index
Hash tables are not sorted data structures, and there are many types of queries which hash indexes can not even help with. For instance, suppose you want to find out all of the employees who are less than 40 years old. How could you do that with a hash table index? Well, it’s not possible because a hash table is only good for looking up key value pairs – which means queries that check for equality
What exactly is inside a database index?
So, now you know that a database index is created on a column in a table, and that the index stores the values in that specific column. But, it is important to understand that a database index does not store the values in the other columns of the same table. For example, if we create an index on the Employee_Name column, this means that the Employee_Age and Employee_Address column values are not also stored in the index. If we did just store all the other columns in the index, then it would be just like creating another copy of the entire table – which would take up way too much space and would be very inefficient.
How does a database know when to use an index?
When a query like “SELECT * FROM Employee WHERE Employee_Name = ‘Abc’ ” is run, the database will check to see if there is an index on the column(s) being queried. Assuming the Employee_Name column does have an index created on it, the database will have to decide whether it actually makes sense to use the index to find the values being searched – because there are some scenarios where it is actually less efficient to use the database index, and more efficient just to scan the entire table.
What is the cost of having a database index?
It takes up space – and the larger your table, the larger your index. Another performance hit with indexes is the fact that whenever you add, delete, or update rows in the corresponding table, the same operations will have to be done to your index. Remember that an index needs to contain the same up to the minute data as whatever is in the table column(s) that the index covers.
As a general rule, an index should only be created on a table if the data in the indexed column will be queried frequently.
See also
What columns generally make good indexes?
How do database indexes work
Simple Description!
The index is nothing but a data structure that stores the values for a specific column in a table. An index is created on a column of a table.
Example: We have a database table called User with three columns – Name, Age and Address. Assume that the User table has thousands of rows.
Now, let’s say that we want to run a query to find all the details of any users who are named 'John'.
If we run the following query:
SELECT * FROM User
WHERE Name = 'John'
The database software would literally have to look at every single row in the User table to see if the Name for that row is ‘John’. This will take a long time.
This is where index helps us: index is used to speed up search queries by essentially cutting down the number of records/rows in a table that needs to be examined.
How to create an index:
CREATE INDEX name_index
ON User (Name)
An index consists of column values(Eg: John) from one table, and those values are stored in a data structure.
So now the database will use the index to find employees named John
because the index will presumably be sorted alphabetically by the
Users name. And, because it is sorted, it means searching for a name
is a lot faster because all names starting with a “J” will be right
next to each other in the index!
Just think of Database Index as Index of a book.
If you have a book about dogs and you want to find an information about let's say, German Shepherds, you could of course flip through all the pages of the book and find what you are looking for - but this of course is time consuming and not very fast.
Another option is that, you could just go to the Index section of the book and then find what you are looking for by using the Name of the entity you are looking ( in this instance, German Shepherds) and also looking at the page number to quickly find what you are looking for.
In Database, the page number is referred to as a pointer which directs the database to the address on the disk where entity is located. Using the same German Shepherd analogy, we could have something like this (“German Shepherd”, 0x77129) where 0x77129 is the address on the disk where the row data for German Shepherd is stored.
In short, an index is a data structure that stores the values for a specific column in a table so as to speed up query search.