I want to know what value is stored against BTree Index and Bitmap Index in oracle
Search the documentation here for "internal structure of indexes".
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Is there anyway we can improve the Hash index creation as it is taking more time than Btree
I'm running Postgres 9.5 and am playing around with BRIN indexes. I have a fact table with about 150 million rows and I'm trying to get PG to use a BRIN index. My query is:
select sum(transaction_amt),
sum (total_amt)
from fact_transaction
where transaction_date_key between 20170101 and 20170201
I created both a BTREE index and a BRIN index (default pages_per_range value of 128) on column transaction_date_key (the above query is referring to January to February 2017). I would have thought that PG would choose to use the BRIN index however it goes with the BTREE index. Here is the explain plan:
https://explain.depesz.com/s/uPI
I then deleted the BTREE index, did a vacuum / analyze on the the table, and re-ran the query and it did choose the BRIN index however the run time was considerably longer:
https://explain.depesz.com/s/5VXi
In fact my tests were all faster when using the BTREE index rather than the BRIN index. I thought it was supposed to be the opposite?
I'd prefer to use the BRIN index because of its smaller size however I can't seem to get PG to use it.
Note: I loaded the data, starting from January 2017 through to June 2017 (defined via transaction_date_key) as I read that physical table ordering makes a difference when using BRIN indexes.
Does anyone know why PG is choosing to use the BTREE index and why BRIN is so much slower in my case?
It seems like the BRIN index scan is not very selective – it returns 30 million rows, all of which have to be re-checked, which is where the time is spent.
That probably means that transaction_date_key is not well correlated with the physical location of the rows in the table.
A BRIN index works by “lumping together” ranges of table blocks (how many can be configured with the storage parameter pages_per_range, whose default value is 128). The maximum and minimum of the indexed value for eatch range of blocks is stored.
So a lot of block ranges in your table contain transaction_date_key between 20170101 and 20170201, and all of these blocks have to be scanned to compute the query result.
I see two options to improve the situation:
Lower the pages_per_range storage parameter. That will make the index bigger, but it will reduce the number of “false positive” blocks.
Cluster the table on the transaction_date_key attribute. As you have found out, that requires (at least temporarily) a B-tree index on the column.
On https://www.postgresql.org/docs/current/static/pgtrgm.html it is explained how special GIN idexes with gin_trgm_ops option can be used to facilitate trigram similarity operator performance.
CREATE INDEX trgm_idx ON test_trgm USING GIN (t gin_trgm_ops);
It is also said:
These indexes do not support equality nor simple comparison operators,
so you may need a regular B-tree index too.
However, there is also BTREE_GIN extension which should allow GIN indexes to be used as substitute for BTREE indexes. https://www.postgresql.org/docs/current/static/btree-gin.html
My question is: If I install BTREE_GIN extension, could pg_trgm GIN index (with gin_trgm_ops option) be used as substitute for BTREE index? Does it combine properties of both BTREE_GIN and trigram GIN index, or additional BTREE index is still needed for joining and equality expressions etc.?
No, if you install btree_gin, you can create a GIN index over “basic” data types like integer, varchar or text.
This is normally useless, since you can use such an index for nothing that wouldn't be done better by a regular B-tree index, but it is very useful if you want to create a multicolumn GIN index including a column with such a data type, for example if you want to create a combined index for an expression like tscol ## to_tsquery('big data') AND intcol = 42.
I'm not trying to restart the UUID vs serial integer key debate. I know there are valid points to either side. I'm using UUID's as the primary key in several of my tables.
Column type: "uuidKey" text NOT NULL
Index: CREATE UNIQUE INDEX grand_pkey ON grand USING btree ("uuidKey")
Primary Key Constraint: ADD CONSTRAINT grand_pkey PRIMARY KEY ("uuidKey");
Here is my first question; with PostgreSQL 9.4 is there any performance benefit to setting the column type to UUID?
The documentation http://www.postgresql.org/docs/9.4/static/datatype-uuid.html describes UUID's, but is there any benefit aside from type safety for using this type instead of text type? In the character types documentation it indicates that char(n) would not have any advantage over text in PostgreSQL.
Tip: There is no performance difference among these three types, apart
from increased storage space when using the blank-padded type, and a
few extra CPU cycles to check the length when storing into a
length-constrained column. While character(n) has performance
advantages in some other database systems, there is no such advantage
in PostgreSQL; in fact character(n) is usually the slowest of the
three because of its additional storage costs. In most situations text
or character varying should be used instead.
I'm not worried about disk space, I'm just wondering if it's worth my time benchmarking UUID vs text column types?
Second question, hash vs b-tree indexes. No sense in sorting UUID keys so would b-tree have any other advantages over hash index?
We had a table with about 30k rows that (for a specific unrelated architectural reason) had UUIDs stored in a text field and indexed. I noticed that the query perf was slower than I'd have expected. I created a new UUID column, copied in the text uuid primary key and compared below. 2.652ms vs 0.029ms. Quite a difference!
-- With text index
QUERY PLAN
Index Scan using tmptable_pkey on tmptable (cost=0.41..1024.34 rows=1 width=1797) (actual time=0.183..2.632 rows=1 loops=1)
Index Cond: (primarykey = '755ad490-9a34-4c9f-8027-45fa37632b04'::text)
Planning time: 0.121 ms
Execution time: 2.652 ms
-- With a uuid index
QUERY PLAN
Index Scan using idx_tmptable on tmptable (cost=0.29..2.51 rows=1 width=1797) (actual time=0.012..0.013 rows=1 loops=1)
Index Cond: (uuidkey = '755ad490-9a34-4c9f-8027-45fa37632b04'::uuid)
Planning time: 0.109 ms
Execution time: 0.029 ms
A UUID is a 16 bytes value. The same as text is a 32 bytes value. The storage sizes are:
select
pg_column_size('a0eebc999c0b4ef8bb6d6bb9bd380a11'::text) as text_size,
pg_column_size('a0eebc999c0b4ef8bb6d6bb9bd380a11'::uuid) as uuid_size;
text_size | uuid_size
-----------+-----------
36 | 16
Smaller tables lead to faster operations.
I am trying to understand how non-clustered index looks up data in a table. I think, the index when non-clustered, simply points to a data page or a group of data rows when doing lookups on the indexed column value rather than pointing to an individual row. In other words, we still need to scan a data page to get the specific data row we are looking up when using non-clustered index. Is this true?
If the non-clustered index is defined on a HEAP (table without clustered index) each index entry contains the row address: (FileId:PageNumber:SlotNumber)
So in this case there is no searching necessary.
If the non-clustered index is defined on a table with a clustered index, each row in the non-clustered index contains the clustered index key. To get to the row SQL Server then has to execute a seek on the clustered index to find the containing page and then a scan of the rows within that page.