In oracle 12c while trying to fetch data with doing multiple join and filtering the data, then doing pagination using rownum, the tables first gets joined and filtered and then result set ordered with order by and then data is fetched. so in case of large number of rows all the join operations are very costly. Even though we need just 100 out of 1 million result set still all data need to be prepared. Any idea to improvise this process?
If you only need a subset of data you could hint query with FIRST_ROWS(n).
Improved Response Time with FIRST_ROWS(n) Hint for ORDER BY Queries
You use the FIRST_ROWS(n) hint in cases where you want the first number (n) of rows in the shortest possible time.
Related
Checking the query cost on a table with 1 million records results in full table scan while the same query in oracle with actual values results in significant lesser cost.
Is this expected behaviour from Oracle ?
Is there a way to tell Oracle not to scan the full table ?
The query is scanning the full table when bind variables are used:
The query cost reduces significantly with actual variables:
This is a pagination query. You want to retrieve a handful of records from the table, filtering on their position in the filtered set. Your projection includes all the columns of the table, so you need to query the table to get the whole row. The question is, why do the two query variants have different plans?
Let's consider the second query. You are passing hard values for the offsets, so the optimizer knows that you want the eleven most recent rows in the sorted set. The set is sorted by an indexed column. The most important element is that the optimizer knows you want 11 rows. 11 is a very small sliver of one million, so using an indexed read to get the required rows is an efficient way of doing things. The path starts at the far end of the index, reads the last eleven entries and retrieves the rows.
Now, your first query has bind variables for the starting and finishing offsets and also for the number of rows to be returned. This is crucial: the optimizer doesn't know whether you want to return eleven rows or eleven thousand rows. So it opts for a very high cardinality. The reason for this is that index reads perform very badly for retrieving large numbers of rows. Full table scans are the best way of handling big slices of our tables.
Is this expected behaviour from Oracle ?
Now you understand this you will can see that the answer to this question is yes. The optimizer makes the best decision it can with the information we give it. When we provide hard values it can be very clever. When we provide vague data it has to guess; sometimes its guesses aren't the ones we expected.
Bind variables are very useful for running the same query with different values when the expected result set is similar. But using bind variables to specify ranges means the result sets can potentially vary tremendously in size.
Is there a way to tell Oracle not to scan the full table ?
If you can fix the pagesize, thus removing the :a2 parameter, that would allow the optimizer to produce a much more accurate plan. Alternatively, if you need to vary the pagesize within a small range (say 10 - 100) then you could try a /*+ cardinality (100) */ hint in the query; provided the cardinality value is within the right order of magnitude it doesn't have to be the precise value.
As with all performance questions, the devil is in the specifics. So you need to benchmark various performance changes and choose the best fit for your particular use case(s).
My users table doesn't have many rows... yet. 😏
Might the query plan of the same query change as the table grows?
I.e., to see how my application will scale, should I seed my users table with BILLIONS 🤑 of rows before using EXPLAIN?
Estimated row counts are probably the most important factor that influence which query plan is chosen.
Two examples that support this:
If you use a WHERE condition on an indexed column of a table, three things can happen:
If the table is very small or a high percentage of the rows match the condition, a sequential scan will be used to read the whole table and filter out the rows that match the condition.
If the table is large and a low percentage of the rows match the condition, an index scan will be used.
If the table is large and a medium percentage of rows match the condition, a bitmap index scan will be used.
If you join two tables, the estimated row counts on the tables will determine if a nested loop join is chosen or not.
I found some bottleneck of my query which select data from only single table then require time and i used non unique key index on two column and with column used in where clause.
select name ,isComplete from Student where year='2015' and isComplete='F'
Now i found some concept from internet like skewed column so what is it?
have an idea then plz help me?
and how to resolve problem of skewed column?
and how skewed column affect performance of the Query?
Skewed columns are columns in which the data is not evenly distributed among the rows.
For example, suppose:
You have a table order_lines with 100,000,000 rows
The table has a column named customer_id
You have 1,000,000 distinct customers
Some (very large) customers can have hundreds of thousands or millions of order lines.
In the above example, the data in order_lines.customer_id is skewed. On average, you'd expect each distinct customer_id to have 100 order lines (100 million rows divided by 1 million distinct customers). But some large customers have many, many more than 100 order lines.
This hurts performance because Oracle bases its execution plan on statistics. So, statistically speaking, Oracle thinks it can access order_lines based on a non-unique index on customer_id and get only 100 records back, which it might then join to another table or whatever using a NESTED LOOP operation.
But, then when it actually gets 1,000,000 order lines for a particular customer, the index access and nested loop join are hideously slow. It would have been far better for Oracle to do a full table scan and hash join to the other table.
So, when there is skewed data, the optimal access plan depends on which particular customer you are selecting!
Oracle lets you avoid this problem by optionally gathering "histograms" on columns, so Oracle knows which values have lots of rows and which have only a few. That gives the Oracle optimizer the information it needs to generate the best plan in most cases.
Full table scan and Index Scan both are depend on the Skewed column.
and Skewed column is nothing but your spread like gender column contain 60 male and 40 female.
I have to sum a huge number of data with aggregation and where clause, using this query
what I am doing is like this : I have three tables one contains terms the second contains user terms , and the third contains correlation factor between term and user term.
I want to calculate the similarity between the sentence that that user inserted with an already existing sentences, and take the results greater than .5 by summing the correlation factor between sentences' terms
The problem is that this query takes more than 15 min. because I have huge tables
any suggestions to improve performance please?
insert into PLAG_SENTENCE_SIMILARITY
SELECT plag_TERMS.SENTENCE_ID ,plag_User_TERMS.SENTENCE_ID,
least( sum( plag_TERM_CORRELATIONS3.CORRELATION_FACTOR)/ plag_terms.sentence_length,
sum (plag_TERM_CORRELATIONS3.CORRELATION_FACTOR)/ plag_user_terms.sentence_length),
plag_TERMs.isn,
plag_user_terms.isn
FROM plag_TERM_CORRELATIONS3,
plag_TERMS,
Plag_User_TERMS
WHERE ( Plag_TERMS.TERM_ROOT = Plag_TERM_CORRELATIONS3.TERM1
AND Plag_User_TERMS.TERM_ROOT = Plag_TERM_CORRELATIONS3.TERM2
AND Plag_User_Terms.ISN=123)
having
least( sum( plag_TERM_CORRELATIONS3.CORRELATION_FACTOR)/ plag_terms.sentence_length,
sum (plag_TERM_CORRELATIONS3.CORRELATION_FACTOR)/ plag_user_terms.sentence_length) >0.5
group by (plag_User_TERMS.SENTENCE_ID,plag_TERMS.SENTENCE_ID , plag_TERMs.isn, plag_terms.sentence_length,plag_user_terms.sentence_length, plag_user_terms.isn);
plag_terms contains more than 50 million records and plag_correlations3 contains 500000
If you have a sufficient amount of free disk space, then create a materialized view
over the join of the three tables
fast-refreshable on commit (don't use the ANSI join syntax here, even if tempted to do so, or the mview won't be fast-refreshable ... a strange bug in Oracle)
with query rewrite enabled
properly physically organized for quick calculations
The query rewrite is optional. If you can modify the above insert-select, then you can just select from the materialized view instead of selecting from the join of the three tables.
As for the physical organization, consider
hash partitioning by Plag_User_Terms.ISN (with a sufficiently high number of partitions; don't hesitate to partition your table with e.g. 1024 partitions, if it seems reasonable) if you want to do a bulk calculation over all values of ISN
single-table hash clustering by Plag_User_Terms.ISN if you want to retain your calculation over a single ISN
If you don't have a spare disk space, then just hint your query to
either use nested loops joins, since the number of rows processed seems to be quite low (assumed by the estimations in the execution plan)
or full-scan the plag_correlations3 table in parallel
Bottom line: Constrain your tables with foreign keys, check constraints, not-null constraints, unique constraints, everything! Because Oracle optimizer is capable of using most of these informations to its advantage, as are the people who tune SQL queries.
I have an application that collects performance metrics and stores them in a datamart. I then use Mondrian to enable analysis and ad-hoc exploration of the data. I'm collecting about 5e6 rows per day and total size of the METRIC table is about 300M rows.
We "color" our data based on the metrics comparison to an SLA. There are exactly 5 distinct values for color. When we do simple MDX queries to get, for example, a color distribution of the data for a specific date range, say 1 day, we see queries like below:
2014-06-11 23:17:08,042 DEBUG [sql] - 223: SqlTupleReader.readTuples
[[Color].[Color]]: executing sql [select "METRIC"."COLOR" as "c0"
from "METRIC" "METRIC" group by "METRIC"."COLOR" order by
"METRIC"."COLOR" ASC NULLS LAST] 2014-06-11 23:17:58,747 DEBUG [sql] -
223: , exec 50704 ms
In order to improve performance, the datamart includes aggregate tables at the hour and day levels, and both aggregate tables include the COLOR column.
I understand that Mondrian is very dependent on the underlying database performance, but there is really no way to tune this. I can create an index on COLOR (because a full scan of the index will be marginally faster than a full scan of the table), but it seems silly to create an index with 5 distinct value on a 300M row table. The day aggregate table has about 500K rows and would be significantly faster executing virtually the same query against this table, but Mondrian always seems to go to the base fact table for these dimension queries.
My question is, is there some way to avoid this query? If I can't avoid it, is it possible to get Mondrian to use the aggregate tables for this type of query? I have specified approxRowCount in the single level of this dimension/hierarchy and that eliminated the similar query to get the count of values. I haven't dug into the source of Mondrian yet to determine if there is a possibility of using the aggregate table or if there is some configuration on my part that is preventing it.
Edit for Clarification:
I probably didn't do a good job of asking my question-let me try and clarify. My MDX query looks something like:
select [Color].[Color].Members on columns,
{[Measures].[Metric Value], [Measures].[Count]} on rows
from [Metric]
where [Time].[2014].[June].[11]
I can look at this and hand write a SQL query that answers this query
select COLOR, avg(VALUE), sum(FACT_COUNT)
from AGG_DAY_METRIC
where YEAR = 2014
and MONTH = 6
and DAY_OF_MONTH = 11
group by COLOR
The database answers this query in about 100ms scanning approx 4K rows. It takes Mondrian several minutes to answer the
query because it does several queries that don't answer the MDX query directly, but rather get information about the
dimension. In the case above, the database has to scan 300M rows, taking 50 seconds, to return that there are 5 possible
colors. If color was in a normal dimension table there would only be 5 rows, but in a degenerate dimension there can be 100s
of millions of rows.
So my questions are:
a) Is there a way to tell Mondrian the values of a degenerate dimension and avoid these queries?
b) Is there a way to have Mondrian answer these queries from aggregate tables?
This problem was solved, not by modifying anything in the Mondrian schema or the application, but the database. The database in this case was Oracle and we were able to create a materialized view with query rewrite enabled.
The materialized view is created from the exact query issued by Mondrian. Since the color values don't change very frequently (almost never in our case), the materialized view does a full refresh once a day.
In this case the queries went from taking minute(s) to milliseconds. If your facing an issue like this and your database is Oracle this is a good approach to speeding up the tuples resolution for degenerate dimensions with low cardinality.
It's hard to give any specific directions without knowing more about your schema, but it looks to me you have to make sure that the number of rows with certain colours (count) has to be marked defined as an aggregate measure (Count or Max Number).
Please note that these aggregates are not calculated continuously (I think it would be to heavy for the backing data-store, and Mondrian won't keep a flowing set in memory for incoming facts).
The aggregation can be specified to be ran/rebuilt at specific times (nightly, hourly...). This would make Mondrian a bit unsuitable for real-time analysis, but you should be able to do almost instant queries on historical data.
If your dimension has 5 distinct values in a 300M fact table it should not be a degenerate dimension. It should be in a separate dimension table. A degenerate dimension should ONLY be used if its cardinality is close to the full fact table row count, making a separate table pointless, as there would be no significant storage savings and joining the dimension results in a lot of data being read;
If you put the colors on a separate dim table, any "Read Tuples" query will return results in a few ms, and your problem is solved.
However, more to the point of your question, Mondrian should be able to pick the dim values from the agg tables. Unless you have distinct-count aggregators in the cube, in which case you're in a tricky situation (unless there's an agg table that exactly matches the level of detail you need, Mondrian will very likely scan the fact table).
You should also set the highCardinality attribute of this degenerate dimension to True. Even with only 5 distinct values, having highCardinality=false tells Mondrian it's safe to scan the whole dimension to populate the list of members. Setting it to true stops this scan.
You should also add an index to this column. It's always a good idea to add indexes to every key and degenerate dimension column in a fact table. With an index the DB should answer much faster that SQL query.
Finally, you have a 300M row fact table. What DBMS are you using? Is it a Column oriented DB? If not, you should try them as a possible alternative to your data store. Column oriented DB have a significant performance increase over Row oriented DBs for Mondrian-like queries. There are a few good options out there, you should test drive them.