Tarkka kuvaus tietokannan indeksoinnista

Suorituskyky on erittäin tärkeä monissa kulutustuotteissa, kuten verkkokaupassa, maksujärjestelmissä, peleissä, kuljetusohjelmissa ja niin edelleen. Vaikka tietokannat on optimoitu sisäisesti useilla mekanismeilla täyttääkseen niiden suorituskykyvaatimukset nykymaailmassa, paljon riippuu myös sovelluskehittäjistä - vain kehittäjä tietää loppujen lopuksi sovelluksen kyselyt.

Relaatiotietokantoja käsittelevät kehittäjät ovat käyttäneet tai ainakin kuulleet indeksoinnista, ja se on hyvin yleinen käsite tietokantamaailmassa. Tärkein osa on kuitenkin ymmärtää, mitä indeksoidaan ja miten indeksointi lisää kyselyn vasteaikaa. Tätä varten sinun on ymmärrettävä, miten aiot kysyä tietokantataulukoita. Oikea hakemisto voidaan luoda vain, kun tiedät tarkalleen miltä kyselysi ja tietojen käyttömallisi näyttävät.

Yksinkertaisella terminologialla hakemisto yhdistää hakunäppäimet vastaaviin tietoihin levyllä käyttämällä erilaisia ​​muistin sisäisiä ja levyllä olevia tietorakenteita. Hakemistoa käytetään nopeuttamaan hakua vähentämällä haettavien tietueiden määrää.

Enimmäkseen hakemisto luodaan WHEREkyselyn lausekkeessa määritetyille sarakkeille, kun tietokanta hakee ja suodattaa taulukoiden tietoja kyseisten sarakkeiden perusteella. Jos et luo hakemistoa, tietokanta skannaa kaikki rivit, suodattaa vastaavat rivit ja palauttaa tuloksen. Miljoonilla tietueilla tämä skannaustoiminto voi viedä useita sekunteja ja tämä korkea vasteaika tekee API: ista ja sovelluksista hitaampia ja käyttökelvottomia. Katsotaanpa esimerkki -

Käytämme MySQL: ää oletusarvoisen InnoDB-tietokantamoottorin kanssa, vaikka tässä artikkelissa selitetyt käsitteet ovat suunnilleen samat muissa tietokantapalvelimissa, kuten Oracle, MSSQL jne.

Luo taulukko, jonka nimi index_demoon seuraava kaava:

CREATE TABLE index_demo ( name VARCHAR(20) NOT NULL, age INT, pan_no VARCHAR(20), phone_no VARCHAR(20) );

Kuinka voimme varmistaa, että käytämme InnoDB-moottoria?

Suorita seuraava komento:

SHOW TABLE STATUS WHERE name = 'index_demo' \G;

EngineSarake edellä näyttökuvan edustaa moottoria, jota käytetään luomaan pöytään. Tätä InnoDBkäytetään.

Lisää nyt satunnaisia ​​tietoja taulukkoon, taulukko, jossa on 5 riviä, näyttää seuraavalta:

En ole tähän mennessä luonut yhtään hakemistoa. Katsotaan tarkistaa tämän komennolla: SHOW INDEX. Se palauttaa 0 tulosta.

Jos suoritamme yksinkertaisen SELECTkyselyn tällä hetkellä, koska käyttäjän määrittelemää hakemistoa ei ole, kysely skannaa koko taulukon saadakseen tuloksen:

EXPLAIN SELECT * FROM index_demo WHERE name = 'alex';

EXPLAINnäyttää kuinka kyselymoottori aikoo suorittaa kyselyn. Yllä olevassa kuvakaappauksessa näet, että rowssarake palaa 5ja possible_keyspalaa null. possible_keysedustaa kaikkia käytettävissä olevia indeksejä, joita voidaan käyttää tässä kyselyssä. keySarake edustaa joka indeksi on todella aiotaan käyttää pois kaikki mahdolliset indeksien tämän kyselyn.

Pääavain:

Yllä oleva kysely on hyvin tehoton. Optimoidaan tämä kysely. Teemme phone_nosarakkeesta PRIMARY KEYolettaen, että järjestelmässämme ei voi olla kahta käyttäjää samalla puhelinnumerolla. Ota huomioon seuraava, kun luot ensisijaisen avaimen:

  • Ensisijaisen avaimen tulisi olla osa sovelluksen tärkeitä kyselyitä.
  • Ensisijainen avain on rajoitus, joka yksilöi taulukon jokaisen rivin. Jos useita sarakkeita on osa ensisijaista avainta, yhdistelmän tulisi olla yksilöllinen kullekin riville.
  • Ensisijaisen avaimen tulee olla Ei-nolla. Älä koskaan tee nollakelpoisista kentistä ensisijaista avainta. ANSI SQL -standardien mukaan ensisijaisten avainten tulisi olla vertailukelpoisia, ja sinun on ehdottomasti pystyttävä selvittämään, onko tietyn rivin ensisijaisen avaimen sarakkeen arvo suurempi, pienempi vai yhtä suuri kuin toinen rivi. Koska NULLtarkoittaa määrittelemätöntä arvoa SQL-standardeissa, et voi deterministisesti verrata NULLmihinkään muuhun arvoon, joten loogisesti NULLei sallita.
  • Ihanteellisen ensisijaisen avaimen tyypin tulisi olla numero, joka on samankaltainen INTtai BIGINTkoska kokonaislukujen vertailu on nopeampaa, joten indeksin läpi kulkeminen on erittäin nopeaa.

Usein määrittelemme idkentän kuten AUTO INCREMENTtaulukoissa ja käytämme sitä ensisijaisena avaimena, mutta ensisijaisen avaimen valinta riippuu kehittäjistä.

Entä jos et luo itse mitään avainta?

Ensisijaisen avaimen luominen ei ole pakollista itse. Jos et ole määritellyt mitään ensisijaista avainta, InnoDB luo sellaisen implisiittisesti sinulle, koska InnoDB: n suunnittelun on oltava ensisijainen avain jokaisessa taulukossa. Joten kun olet luonut ensisijaisen avaimen myöhemmin tälle taululle, InnoDB poistaa aiemmin automaattisesti määritetyn ensisijaisen avaimen.

Koska meillä ei ole vielä määritelty ensisijaista avainta, katsotaanpa, mitä InnoDB oletusarvoisesti loi meille:

SHOW EXTENDED INDEX FROM index_demo;

EXTENDED näyttää kaikki indeksit, joita käyttäjä ei voi käyttää, mutta joita MySQL hallitsee kokonaan.

Tässä näemme, että MySQL on määritellyt komposiitti-indeksi (aiomme keskustella yhdistettyjen indeksien myöhemmin) päällä DB_ROW_ID, DB_TRX_ID, DB_ROLL_PTR, ja kaikki sarakkeet on määritelty taulukossa. Jos käyttäjän määrittelemää ensisijaista avainta ei ole, tätä hakemistoa käytetään tietueiden yksilöimiseen.

Mitä eroa on avain ja hakemisto?

Vaikka termejä key& indexkäytetään keskenään, se keytarkoittaa sarakkeen käyttäytymiselle asetettua rajoitusta. Tässä tapauksessa rajoituksena on, että ensisijainen avain ei ole nollakelpoinen kenttä, joka yksilöi jokaisen rivin. Toisaalta, indexse on erityinen tietorakenne, joka helpottaa tietojen hakua koko taulukosta.

Luodaan nyt ensisijainen hakemisto phone_noja tutkitaan luotua hakemistoa:

ALTER TABLE index_demo ADD PRIMARY KEY (phone_no); SHOW INDEXES FROM index_demo;

Huomaa, että CREATE INDEXsitä ei voida käyttää ensisijaisen indeksin luomiseen, mutta ALTER TABLEsitä käytetään.

Yllä olevassa kuvakaappauksessa näemme, että sarakkeeseen luodaan yksi ensisijainen hakemisto phone_no. Seuraavien kuvien sarakkeet kuvataan seuraavasti:

Table : Taulukko, johon hakemisto luodaan.

Non_unique: Jos arvo on 1, indeksi ei ole yksilöllinen, jos arvo on 0, indeksi on ainutlaatuinen.

Key_name: Luodun hakemiston nimi. Ensisijaisen hakemiston nimi on aina PRIMARYMySQL: ssä riippumatta siitä, oletko antanut jonkin hakemiston nimen hakemistoa luodessasi.

Seq_in_index: Hakemiston sarakkeen järjestysnumero. Jos hakemistoon kuuluu useita sarakkeita, järjestysnumero annetaan sen mukaan, miten sarakkeet järjestettiin indeksin luomisen aikana. Järjestysnumero alkaa 1: stä.

Collation: how the column is sorted in the index. A means ascending, D means descending, NULL means not sorted.

Cardinality: The estimated number of unique values in the index. More cardinality means higher chances that the query optimizer will pick the index for queries.

Sub_part : The index prefix. It is NULL if the entire column is indexed. Otherwise, it shows the number of indexed bytes in case the column is partially indexed. We will define partial index later.

Packed: Indicates how the key is packed; NULL if it is not.

Null: YES if the column may contain NULL values and blank if it does not.

Index_type: Indicates which indexing data structure is used for this index. Some possible candidates are — BTREE, HASH, RTREE, or FULLTEXT.

Comment: The information about the index not described in its own column.

Index_comment: The comment for the index specified when you created the index with the COMMENT attribute.

Now let’s see if this index reduces the number of rows which will be searched for a given phone_no in the WHERE clause of a query.

EXPLAIN SELECT * FROM index_demo WHERE phone_no = '9281072002';

In this snapshot, notice that the rows column has returned 1 only, the possible_keys & key both returns PRIMARY . So it essentially means that using the primary index named as PRIMARY (the name is auto assigned when you create the primary key), the query optimizer just goes directly to the record & fetches it. It’s very efficient. This is exactly what an index is for — to minimize the search scope at the cost of extra space.

Clustered Index:

A clustered index is collocated with the data in the same table space or same disk file. You can consider that a clustered index is a B-Tree index whose leaf nodes are the actual data blocks on disk, since the index & data reside together. This kind of index physically organizes the data on disk as per the logical order of the index key.

What does physical data organization mean?

Physically, data is organized on disk across thousands or millions of disk / data blocks. For a clustered index, it’s not mandatory that all the disk blocks are contagiously stored. Physical data blocks are all the time moved around here & there by the OS whenever it’s necessary. A database system does not have any absolute control over how physical data space is managed, but inside a data block, records can be stored or managed in the logical order of the index key. The following simplified diagram explains it:

  • The yellow coloured big rectangle represents a disk block / data block
  • the blue coloured rectangles represent data stored as rows inside that block
  • the footer area represents the index of the block where red coloured small rectangles reside in sorted order of a particular key. These small blocks are nothing but sort of pointers pointing to offsets of the records.

Records are stored on the disk block in any arbitrary order. Whenever new records are added, they get added in the next available space. Whenever an existing record is updated, the OS decides whether that record can still fit into the same position or a new position has to be allocated for that record.

So position of records are completely handled by OS & no definite relation exists between the order of any two records. In order to fetch the records in the logical order of key, disk pages contain an index section in the footer, the index contains a list of offset pointers in the order of the key. Every time a record is altered or created, the index is adjusted.

In this way, you really don’t need to care about actually organizing the physical record in a certain order, rather a small index section is maintained in that order & fetching or maintaining records becomes very easy.

Advantage of Clustered Index:

This ordering or co-location of related data actually makes a clustered index faster. When data is fetched from disk, the complete block containing the data is read by the system since our disk IO system writes & reads data in blocks. So in case of range queries, it’s quite possible that the collocated data is buffered in memory. Say you fire the following query:

SELECT * FROM index_demo WHERE phone_no > '9010000000' AND phone_no < '9020000000'

A data block is fetched in memory when the query is executed. Say the data block contains phone_no in the range from 9010000000 to 9030000000 . So whatever range you requested for in the query is just a subset of the data present in the block. If you now fire the next query to get all the phone numbers in the range, say from 9015000000 to 9019000000 , you don’t need to fetch any more blocks from the disk. The complete data can be found in the current block of data, thus clustered_index reduces the number of disk IO by collocating related data as much as possible in the same data block. This reduced disk IO causes improvement in performance.

So if you have a well thought of primary key & your queries are based on the primary key, the performance will be super fast.

Constraints of Clustered Index:

Koska klusteroitu indeksi vaikuttaa tietojen fyysiseen organisaatioon, taulukoita voi olla vain yksi klusteroitu indeksi.

Suhde ensisijaisen avaimen ja klusteroidun indeksin välillä:

Et voi luoda klusteroitua hakemistoa manuaalisesti käyttämällä InnoDB: tä MySQL: ssä. MySQL valitsee sen sinulle. Mutta miten se valitsee? Seuraavat otteet ovat MySQL-dokumentaatiosta:

Kun määrität PRIMARY KEYtaulukossa a- InnoDBmerkinnän , käytä sitä klusteroituna hakemistona. Määritä jokaiselle luomallesi taulukolle ensisijainen avain. Jos loogista yksilöllistä ja ei-nollasaraketta tai sarakesarjaa ei ole, lisää uusi automaattisen lisäyksen sarake, jonka arvot täytetään automaattisesti.

Jos et määritä PRIMARY KEYtaulukkoon a- merkintää, MySQL etsii ensimmäisen UNIQUEhakemiston, jossa kaikki avainsarakkeet ovat, NOT NULLja InnoDBkäyttää sitä klusteroituna hakemistona.

If the table has no PRIMARY KEY or suitable UNIQUE index, InnoDB internally generates a hidden clustered index named GEN_CLUST_INDEX on a synthetic column containing row ID values. The rows are ordered by the ID that InnoDB assigns to the rows in such a table. The row ID is a 6-byte field that increases monotonically as new rows are inserted. Thus, the rows ordered by the row ID are physically in insertion order.

In short, the MySQL InnoDB engine actually manages the primary index as clustered index for improving performance, so the primary key & the actual record on disk are clustered together.

Structure of Primary key (clustered) Index:

An index is usually maintained as a B+ Tree on disk & in-memory, and any index is stored in blocks on disk. These blocks are called index blocks. The entries in the index block are always sorted on the index/search key. The leaf index block of the index contains a row locator. For the primary index, the row locator refers to virtual address of the corresponding physical location of the data blocks on disk where rows reside being sorted as per the index key.

In the following diagram, the left side rectangles represent leaf level index blocks, and the right side rectangles represent the data blocks. Logically the data blocks look to be aligned in a sorted order, but as already described earlier, the actual physical locations may be scattered here & there.

Is it possible to create a primary index on a non-primary key?

In MySQL, a primary index is automatically created, and we have already described above how MySQL chooses the primary index. But in the database world, it’s actually not necessary to create an index on the primary key column — the primary index can be created on any non primary key column as well. But when created on the primary key, all key entries are unique in the index, while in the other case, the primary index may have a duplicated key as well.

Is it possible to delete a primary key?

It’s possible to delete a primary key. When you delete a primary key, the related clustered index as well as the uniqueness property of that column gets lost.

ALTER TABLE `index_demo` DROP PRIMARY KEY; - If the primary key does not exist, you get the following error: "ERROR 1091 (42000): Can't DROP 'PRIMARY'; check that column/key exists"

Advantages of Primary Index:

  • Primary index based range queries are very efficient. There might be a possibility that the disk block that the database has read from the disk contains all the data belonging to the query, since the primary index is clustered & records are ordered physically. So the locality of data can be provided by the primary index.
  • Any query that takes advantage of primary key is very fast.

Disadvantages of Primary Index:

  • Since the primary index contains a direct reference to the data block address through the virtual address space & disk blocks are physically organized in the order of the index key, every time the OS does some disk page split due to DML operations like INSERT / UPDATE / DELETE, the primary index also needs to be updated. So DML operations puts some pressure on the performance of the primary index.

Secondary Index:

Any index other than a clustered index is called a secondary index. Secondary indices does not impact physical storage locations unlike primary indices.

When do you need a Secondary Index?

You might have several use cases in your application where you don’t query the database with a primary key. In our example phone_no is the primary key but we may need to query the database with pan_no, or name . In such cases you need secondary indices on these columns if the frequency of such queries is very high.

How to create a secondary index in MySQL?

The following command creates a secondary index in the name column in the index_demo table.

CREATE INDEX secondary_idx_1 ON index_demo (name);

Structure of Secondary Index:

In the diagram below, the red coloured rectangles represent secondary index blocks. Secondary index is also maintained in the B+ Tree and it’s sorted as per the key on which the index was created. The leaf nodes contain a copy of the key of the corresponding data in the primary index.

So to understand, you can assume that the secondary index has reference to the primary key’s address, although it’s not the case. Retrieving data through the secondary index means you have to traverse two B+ trees — one is the secondary index B+ tree itself, and the other is the primary index B+ tree.

Advantages of a Secondary Index:

Logically you can create as many secondary indices as you want. But in reality how many indices actually required needs a serious thought process since each index has its own penalty.

Disadvantages of a Secondary Index:

With DML operations like DELETE / INSERT , the secondary index also needs to be updated so that the copy of the primary key column can be deleted / inserted. In such cases, the existence of lots of secondary indexes can create issues.

Also, if a primary key is very large like a URL, since secondary indexes contain a copy of the primary key column value, it can be inefficient in terms of storage. More secondary keys means a greater number of duplicate copies of the primary key column value, so more storage in case of a large primary key. Also the primary key itself stores the keys, so the combined effect on storage will be very high.

Consideration before you delete a Primary Index:

In MySQL, you can delete a primary index by dropping the primary key. We have already seen that a secondary index depends on a primary index. So if you delete a primary index, all secondary indices have to be updated to contain a copy of the new primary index key which MySQL auto adjusts.

This process is expensive when several secondary indexes exist. Also other tables may have a foreign key reference to the primary key, so you need to delete those foreign key references before you delete the primary key.

When a primary key is deleted, MySQL automatically creates another primary key internally, and that’s a costly operation.

UNIQUE Key Index:

Like primary keys, unique keys can also identify records uniquely with one difference — the unique key column can contain null values.

Unlike other database servers, in MySQL a unique key column can have as many null values as possible. In SQL standard, null means an undefined value. So if MySQL has to contain only one null value in a unique key column, it has to assume that all null values are the same.

But logically this is not correct since null means undefined — and undefined values can’t be compared with each other, it’s the nature of null. As MySQL can’t assert if all nulls mean the same, it allows multiple null values in the column.

The following command shows how to create a unique key index in MySQL:

CREATE UNIQUE INDEX unique_idx_1 ON index_demo (pan_no);

Composite Index:

MySQL lets you define indices on multiple columns, up to 16 columns. This index is called a Multi-column / Composite / Compound index.

Let’s say we have an index defined on 4 columns — col1, col2, col3, col4. With a composite index, we have search capability on col1, (col1, col2) , (col1, col2, col3) , (col1, col2, col3, col4). So we can use any left side prefix of the indexed columns, but we can’t omit a column from the middle & use that like — (col1, col3) or (col1, col2, col4) or col3 or col4 etc. These are invalid combinations.

The following commands create 2 composite indexes in our table:

CREATE INDEX composite_index_1 ON index_demo (phone_no, name, age); CREATE INDEX composite_index_2 ON index_demo (pan_no, name, age);

If you have queries containing a WHERE clause on multiple columns, write the clause in the order of the columns of the composite index. The index will benefit that query. In fact, while deciding the columns for a composite index, you can analyze different use cases of your system & try to come up with the order of columns that will benefit most of your use cases.

Composite indices can help you in JOIN & SELECT queries as well. Example: in the following SELECT * query, composite_index_2 is used.

When several indexes are defined, the MySQL query optimizer chooses that index which eliminates the greatest number of rows or scans as few rows as possible for better efficiency.

Why do we use composite indices? Why not define multiple secondary indices on the columns we are interested in?

MySQL uses only one index per table per query except for UNION. (In a UNION, each logical query is run separately, and the results are merged.) So defining multiple indices on multiple columns does not guarantee those indices will be used even if they are part of the query.

MySQL maintains something called index statistics which helps MySQL infer what the data looks like in the system. Index statistics is a generilization though, but based on this meta data, MySQL decides which index is appropriate for the current query.

How does composite index work?

The columns used in composite indices are concatenated together, and those concatenated keys are stored in sorted order using a B+ Tree. When you perform a search, concatenation of your search keys is matched against those of the composite index. Then if there is any mismatch between the ordering of your search keys & ordering of the composite index columns, the index can’t be used.

In our example, for the following record, a composite index key is formed by concatenating pan_no, name, ageHJKXS9086Wkousik28.

+--------+------+------------+------------+ name age pan_no phone_no +--------+------+------------+------------+ kousik 28 HJKXS9086W 9090909090

How to identify if you need a composite index:

  • Analyze your queries first according to your use cases. If you see certain fields are appearing together in many queries, you may consider creating a composite index.
  • If you are creating an index in col1 & a composite index in (col1, col2), then only the composite index should be fine. col1 alone can be served by the composite index itself since it’s a left side prefix of the index.
  • Consider cardinality. If columns used in the composite index end up having high cardinality together, they are good candidate for the composite index.

Covering Index:

A covering index is a special kind of composite index where all the columns specified in the query somewhere exist in the index. So the query optimizer does not need to hit the database to get the data — rather it gets the result from the index itself. Example: we have already defined a composite index on (pan_no, name, age) , so now consider the following query:

SELECT age FROM index_demo WHERE pan_no = 'HJKXS9086W' AND name = 'kousik'

The columns mentioned in the SELECT & WHERE clauses are part of the composite index. So in this case, we can actually get the value of the age column from the composite index itself. Let’s see what the EXPLAIN command shows for this query:

EXPLAIN FORMAT=JSON SELECT age FROM index_demo WHERE pan_no = 'HJKXS9086W' AND name = '111kousik1';

In the above response, note that there is a key — using_index which is set to true which signifies that the covering index has been used to answer the query.

I don’t know how much covering indices are appreciated in production environments, but apparently it seems to be a good optimization in case the query fits the bill.

Partial Index:

We already know that Indices speed up our queries at the cost of space. The more indices you have, the more the storage requirement. We have already created an index called secondary_idx_1 on the column name. The column name can contain large values of any length. Also in the index, the row locators’ or row pointers’ metadata have their own size. So overall, an index can have a high storage & memory load.

In MySQL, it’s possible to create an index on the first few bytes of data as well. Example: the following command creates an index on the first 4 bytes of name. Though this method reduces memory overhead by a certain amount, the index can’t eliminate many rows, since in this example the first 4 bytes may be common across many names. Usually this kind of prefix indexing is supported on CHAR ,VARCHAR, BINARY, VARBINARY type of columns.

CREATE INDEX secondary_index_1 ON index_demo (name(4));

What happens under the hood when we define an index?

Let’s run the SHOW EXTENDED command again:

SHOW EXTENDED INDEXES FROM index_demo;

We defined secondary_index_1 on name, but MySQL has created a composite index on (name, phone_no) where phone_no is the primary key column. We created secondary_index_2 on age & MySQL created a composite index on (age, phone_no). We created composite_index_2 on (pan_no, name, age) & MySQL has created a composite index on (pan_no, name, age, phone_no). The composite index composite_index_1 already has phone_no as part of it.

So whatever index we create, MySQL in the background creates a backing composite index which in-turn points to the primary key. This means that the primary key is a first class citizen in the MySQL indexing world. It also proves that all the indexes are backed by a copy of the primary index —but I am not sure whether a single copy of the primary index is shared or different copies are used for different indexes.

There are many other indices as well like Spatial index and Full Text Search index offered by MySQL. I have not yet experimented with those indices, so I’m not discussing them in this post.

General Indexing guidelines:

  • Since indices consume extra memory, carefully decide how many & what type of index will suffice your need.
  • With DML operations, indices are updated, so write operations are quite costly with indexes. The more indices you have, the greater the cost. Indexes are used to make read operations faster. So if you have a system that is write heavy but not read heavy, think hard about whether you need an index or not.
  • Cardinality is important — cardinality means the number of distinct values in a column. If you create an index in a column that has low cardinality, that’s not going to be beneficial since the index should reduce search space. Low cardinality does not significantly reduce search space.

    Example: if you create an index on a boolean ( int1 or 0 only ) type column, the index will be very skewed since cardinality is less (cardinality is 2 here). But if this boolean field can be combined with other columns to produce high cardinality, go for that index when necessary.

  • Indices might need some maintenance as well if old data still remains in the index. They need to be deleted otherwise memory will be hogged, so try to have a monitoring plan for your indices.

In the end, it’s extremely important to understand the different aspects of database indexing. It will help while doing low level system designing. Many real-life optimizations of our applications depend on knowledge of such intricate details. A carefully chosen index will surely help you boost up your application’s performance.

Taputa ja jaa ystävien kanssa ja sosiaalisessa mediassa, jos pidät tästä artikkelista. :)

Viitteet:

  1. //dev.mysql.com/doc/refman/5.7/fi/innodb-index-types.html
  2. //www.quora.com/Mitä-Ennen-primary-index-and-secondary-index-ex-ly-And-whats-advantage-of-one-over-another
  3. //dev.mysql.com/doc/refman/8.0/en/create-index.html
  4. //www.oreilly.com/library/view/high-performance-mysql/0596003064/ch04.html
  5. //www.unofficialmysqlguide.com/covering-indexes.html
  6. //dev.mysql.com/doc/refman/8.0/fi/multiple-column-indexes.html
  7. //dev.mysql.com/doc/refman/8.0/en/show-index.html
  8. //dev.mysql.com/doc/refman/8.0/en/create-index.html