database-performance

I have a collection faults in my MongoDB database which every document has these fields: rack_name , timestamp Just for sake of testing and comparing performances, I have created these two indexes: rack -> {'rack_name': 1} and time -> {'timestamp': 1} Now I executed the following query with explain(): db.faults.find({ 'rack_name': { $in: [ 'providence1', 'helena2' ] }, 'timestamp': { $gt: 1501548359000 } }) .explain('allPlansExecution') and here is the result: { "queryPlanner" : { "plannerVersion" : 1, "namespace" : "quicktester_clone.faults", "indexFilterSet" : false, "parsedQuery" : { "$and"

I'm trying optimizing a query, trying to avoid repeating the query indicated with " COMPLEX QUERY ", that is used 2 times and both, has the same results. The original query SELECT news.* FROM news INNER JOIN((SELECT myposter FROM (SELECT **COMPLEX QUERY**)) UNION (SELECT myposter FROM `profiles_old` prof2 WHERE prof2.profile_id NOT IN (SELECT **COMPLEX QUERY**))) r ON news.profile = r.p I was wondering if something like this was possible: SELECT news.* FROM (SELECT **COMPLEX QUERY**) complexQuery, news INNER JOIN ((SELECT myposter FROM complexquery) UNION (SELECT myposter FROM `profiles_old`

I have function with two nested cursors. The outer cursor gets payment details of a customer from the source and inserts into the target based on some business logic. The inner cursor takes the payment details of each payment, it happens one after another. The payments table has about 125000 rows, and about 335000 rows for payment details. All of these rows are to be migrated to a target table. Executing the function takes over two hours and the database CPU usage goes up to 99%. I am working with PostgreSQL 9.2. How can I improve the performance of the function? The code I am using: CREATE OR

I was looking into performance of selecting closest points within certain proximity to given coordinate. Options are to ether use two decimal(8,6) - lat, long columns or single geography column and work with that. I am only interested which is faster? Matas Vaitkevicius TL;DR Geography is ~10 times faster. Ok so I have set up test: Couple of tables one with id,lat,long (int, decimal(8,6),decimal(8,6)) other with id,coord (int, geography) . Then insert 47k of random data. For indexing first table I used nonclustered Ascending index on lat,long with fill factor of 95. for second one GRIDS =

The queries are performed on a large table with 11 million rows. I have already performed an ANALYZE on the table prior to the query executions. Query 1: SELECT * FROM accounts t1 LEFT OUTER JOIN accounts t2 ON (t1.account_no = t2.account_no AND t1.effective_date < t2.effective_date) WHERE t2.account_no IS NULL; Explain Analyze: Hash Anti Join (cost=480795.57..1201111.40 rows=7369854 width=292) (actual time=29619.499..115662.111 rows=1977871 loops=1) Hash Cond: ((t1.account_no)::text = (t2.account_no)::text) Join Filter: ((t1.effective_date)::text < (t2.effective_date)::text) -> Seq Scan on

I have a table with many rows, where I periodically want to set one column to 0 using a cron. What is faster / less memory consuming, doing an UPDATE on all rows (ie. no WHERE clause) or doing an UPDATE only WHERE mycolumn != 0 ? chesterbr As noticed in comments on the original post, it depends on several things (index, database engine, type of storage media, available cache memory, etc.). We could make an educated guess that: a) We should always have a full-table scan unless we have an index on the column (and I'd not recommend one just for the sake of this query, as you will penalize down