We have a table that has millions of rows with PostGIS geometries in them. The query we want to perform is: what are the most recent entries that fall within a bounding geometry? The problem with this query is that we'll often have a large number of items that match the bounding box (which has around 5km in radius), and Postgres will then have to re-check all the returned items inside the bounding box to get their timestamp, then sort and return the latest N.
It feels like what we need is a (compound?) index that takes into account both the GIST spatial index and the timestamp as well. Is such a thing possible? I've tried several combinations in the CREATE INDEX step and nothing's worked so far.
I'd rather make two indexes, one spatial and the second on the timestamp column. PostgreSQL can combine indexes pretty nice and it doesn't need to 're-check' found rows. It could use indexes to get the rows within the geometry and sort them using the other index.
Related
I have below query to fetch list of tickets.
EXPLAIN select * from ticket_type
where ticket_type.event_id='89898'
and ticket_type.active=true
and (ticket_type.is_unlimited = true OR ticket_type.number_of_sold_tickets < ticket_type.number_of_tickets)
order by ticket_type.ticket_type_order
I have created below indexes but not working.
Index on (ticket_type_order,event_id,is_unlimited,active)
Index on (ticket_type_order,event_id,active,number_of_sold_tickets,number_of_tickets).
The perfect index for this query would be
CREATE INDEX ON ticket_type (event_id, ticket_type_order)
WHERE active AND (is_unlimited OR number_of_sold_tickets < number_of_tickets);
Of course, a partial index like that might only be useful for this specific query.
If the WHERE conditions from the index definition are not very selective, or a somewhat slower execution is also acceptable, you can omit parts of or the whole WHERE clause. That makes the index more widely useful.
What is the size of the table and usual query result? The server is usually smart enough and disables indexes, if it expects to return more than the half of the table.
Index makes no sense, if the result is rather small. If the server has - let say - 1000 records after several filtration steps, the server stops using indexes. It is cheaper the finish the query using CPU, then loading an index from HDD. As result, indexes are never applied to small tables.
Order by is applied at the very end of the query processing. The first field in the index should be one of the fields from the where filter.
Boolean fields are seldom useful in the index. It has only two possible values. Index should be created for fields with a lot of different values.
Avoid or filtering. It is easy in your case. Put a very big number into number_of_tickets, if the tickets are unlimited.
The better index in your case would be just event_id. If the database server supports functional indexes, then you can try to add number_of_tickets - number_of_sold_tickets. Rewrite the statement as where number_of_tickets - number_of_sold_tickets > 0
UPDATE: Postgresql calls it "Index on Expression":
https://www.postgresql.org/docs/current/indexes-expressional.html
I have a query that has several filter conditions after WHERE clause.
Also, most of the columns involved have indexes on them.
When I run EXPLAIN command, I see:
-> Bitmap Index Scan on feature_expr_idx (cost=0.00..8.10 rows=14 width=0)
feature_expr_idx is an index on one of the columns in WHERE clause.
But indexes for other columns are not shown. Instead, they are shown in FILTER row:
Filter: ((NOT is_deleted) AND (vehicle_type = 'car'::text) AND (source_type = 'NONE'::text))
Why only a single Index is shown in the result, while other columns also having index are instead part of Filter?
Postgresql has a clever engine which tries to plan the best way to run your query. Often, this involves reading as little as possible from disk, because disk operations are slow. One of the reasons why indexes are so helpful is that by reading from the index, we can find a small number of rows in the table that need to be read in order to satisfy the query, and thus we can avoid reading through the entire table. Note, however, that the index is also on disk, and so reading the index also takes some time.
Now, imagine your query has two filters, one over column A and one over column B, both of which are indexed. According to the statistics postgresql has collected, there are about 5 rows that satisfy the filter on column A, and about 1000 rows that satisfy the filter on column B. In that case, it makes sense to read only the index on column A, then read all the matching 5 (or so) rows, and filter out any of them which don't match the filter on column B. Reading the index on column B would probably be more expensive than just reading the 5 rows!
The actual reason may be different than my example, but the point is that postgresql is simply trying to be as efficient as possible.
I could not reach any conclusive answers reading some of the existing posts on this topic.
I have certain data at 100 locations the for past 10 years. The table has about 800 million rows. I need to primarily generate yearly statistics for each location. Some times I need to generate monthly variation statistics and hourly variation statistics as well. I'm wondering if I should generate two indexes - one for location and another for year or generate one index on both location and year. My primary key currently is a serial number (Probably I could use location and timestamp as the primary key).
Thanks.
Regardless of how many indices have you created on relation, only one of them will be used in a certain query (which one depends on query, statistics etc). So in your case you wouldn't get a cumulative advantage from creating two single column indices. To get most performance from index I would suggest to use composite index on (location, timestamp).
Note, that queries like ... WHERE timestamp BETWEEN smth AND smth will not use the index above while queries like ... WHERE location = 'smth' or ... WHERE location = 'smth' AND timestamp BETWEEN smth AND smth will. It's because the first attribute in index is crucial for searching and sorting.
Don't forget to perform
ANALYZE;
after index creation in order to collect statistics.
Update:
As #MondKin mentioned in comments certain queries can actually use several indexes on the same relation. For example, query with OR clauses like a = 123 OR b = 456 (assuming that there are indexes for both columns). In this case postgres would perform bitmap index scans for both indexes, build a union of resulting bitmaps and use it for bitmap heap scan. In certain conditions the same scheme may be used for AND queries but instead of union there would be an intersection.
There is no rule of thumb for situations like these, I suggest you experiment in a copy of your production DB to see what works best for you: a single multi-column index or 2 single-column indexes.
One nice feature of Postgres is you can have multiple indexes and use them in the same query. Check this chapter of the docs:
... PostgreSQL has the ability to combine multiple indexes ... to handle cases that cannot be implemented by single index scans ....
... Sometimes multicolumn indexes are best, but sometimes it's better to create separate indexes and rely on the index-combination feature ...
You can even experiment creating both the individual and combined indexes, and checking how big each one is and determine if it's worth having them at the same time.
Some things that you can also experiment with:
If your table is too large, consider partitioning it. It looks like you could partition either by location or by date. Partitioning splits your table's data in smaller tables, reducing the amount of places where a query needs to look.
If your data is laid out according to a date (like transaction date) check BRIN indexes.
If multiple queries will be processing your data in a similar fashion (like aggregating all transactions over the same period, check materialized views so you only need to do those costly aggregations once.
About the order in which to put your multi-column index, put first the column on which you will have an equality operation, and later the column in which you have a range, >= or <= operation.
An index on (location,timestamp) should work better that 2 separate indexes for you case. Note that the order of the columns is important.
I would like to use the GEO2D function in Sphinx (SphinxQL) but I would like to use it without searching an index.
I have the polygon and the latitude and longitude already and just want to calculate if the point is inside the polygon. So this is what I want:
SELECT CONTAINS(GEOPOLY2D($polygon),$lat_deg, $long_deg)
in MySQL you could run a query without specifying a database:
SELECT 1+1
How could I do this in Sphinx? I already tried to run it with an index (FROM index LIMIT 1) but that seems to do a full table scan and thus take a long time.
I could make a new index with just one record but maybe there is another way?
We have two types of high-volume queries. One looks for docs involving 5 attributes: a date (lte), a value stored in an array, a value stored in a second array, one integer (gte), and one float (gte).
The second includes these five attributes plus two more.
Should we create two compound indices, one for each query? Assume each attribute has a high cardinality.
If we do, because each query involves multiple arrays, it doesn't seem like we can create an index because of Mongo's restriction. How do people structure their Mongo databases in this case?
We're using MongoMapper.
Thanks!
Indexes for queries after the first ranges in the query the value of the additional index fields drops significantly.
Conceptually, I find it best to think of the addition fields in the index pruning ever smaller sub-trees from the query. The first range chops off a large branch, the second a smaller, the third smaller, etc. My general rule of thumb is only the first range from the query in the index is of value.
The caveat to that rule is that additional fields in the index can be useful to aid sorting returned results.
For the first query I would create a index on the two array values and then which ever of the ranges will exclude the most documents. The date field is unlikely to provide high exclusion unless you can close the range (lte and gte). The integer and float is hard to tell without knowing the domain.
If the second query's two additional attributes also use ranges in the query and do not have a significantly higher exclusion value then I would just work with the one index.
Rob.