Our study introduces a novel distributed query plan refinement phase in an enhanced architecture of distributed query processing engine DQPE Query plan refinement generates potentially efficient distributed query plan by reusable aggregate query shipping RAQS approach The approach improves response time at the cost of pre processing time
In the past the database community has proposed two separate ideas sampling based approximate query processing AQP and aggregate precomputation AggPre such as data cubes to address this challenge In this paper we argue for the need to connect these two separate ideas for interactive analytics We propose AQP a novel framework to
3 / 51 Query Processing Recap Access Methods • Access methods are the alternative ways for retrieving specific tuples • We covered two access methods sequential scan and index scan • Sequential scan is done over an unordered table heap • Index scan is done over an ordered B Tree or an unordered hash table • Hash tables are fast data structures that
In this paper we focus on current CPU FPGA architectures and study their usability for database management systems To focus our scope we choose aggregation as the query processing primitive for this investigation We implement a fully pipelined stall free module that performs aggregation on the FPGA and also describe a performance model that predicts the runtime of
Ad hoc Aggregation query is extremely important for data intensive applications in the cloud which extracts valuable summary information on massive datasets to help decision maker make right decisions Current data storage schemes row store and column store cannot efficiently answer ad hoc aggregation on massive data sets in the cloud A new data storage structure bit vector
One Query Two Aggregate Functions The Advanced Approach The advanced version is when the aggregations communicate meaning you first need to find one aggregated value and then use it to calculate another aggregated value Typically this is achieved either through subqueries or Common Table Expressions CTEs
The proposed AQPT significantly outperforms the contemporary technique in terms of estimation accuracy and query execution latency and the efficacy of utilizing Central Limit Theorem in designing the AQPT for processing join aggregate queries on Big Data is investigated Query Processing on Big Data has received significant attention in the literature
Traditional query processing techniques have played a major role in the success of relational Database Management Systems over the last decade However they do not obviously extend to much more challenging unorganized and unpredictable data providers typical
Queries that include aggregate functions are still subject to the limit on total number of query rows All aggregate functions other than COUNT or COUNT fieldname include each row used by the aggregation as a query row for the purposes of limit For COUNT or COUNT fieldname queries limits are counted as one query row unless the query contains a
SQL Aggregate Functions An aggregate function is a function that performs a calculation on a set of values and returns a single value Aggregate functions are often used with the GROUP BY clause of the SELECT statement The GROUP BY clause splits the result set into groups of values and the aggregate function can be used to return a single value for each group
SQL ： SQL 。 ：SQL ？ SQL 。 。
Instead of processing each aggregate query separately the proposed method processes multiple aggregate queries together in a batch by a single optimized MapReduce job which improves the performance significantly compared to a naive method ABSTRACT MapReduce is a widely used programming model for analyzing and processing Big data
The pre computed query Q and the new query q are in the same form suggesting that their aggregation function COUNT the aggregated attribute A and the predicate attribute B are the same According to Eq 1 the estimation of q is the sum of two parts The first part is the true result R Q of the pre computed query second part is the difference between the
The survey can help the partitioners to understand existing AQP techniques and select appropriate methods in their applications and provide research challenges and opportunities of AQP Online analytical processing OLAP is a core functionality in database systems The performance of OLAP is crucial to make online decisions in many applications However it is
A query block contains a single SELECT FROM WHERE expression as well as GROUP BY and HAVING clause if these are part of the block Nested queries within a query are identified as separate query blocks Aggregate operators in
PostgreSQL。 。：（count）、（sum）、（avg）、（max）、（min）。
In [1] the presented Approximation Query Processing Technique AQPT addresses four aggregate operations Sum Average Variance and Standa rd Deviation This AQPT is designed using Map Reduce
In the past the database community has proposed two separate ideas sampling based approximate query processing AQP and aggregate precomputation AggPre such as data cubes to address this
Approximate aggregate query processing techniques presented in [1 2] provide approximate results to a simple non join aggregate query as depicted in Query 1 for Big Data queries
6 The new APPROX COUNT DISTINCT aggregate function returns the approximate number of unique non null values in a group This feature is available starting with SQL Server 2019 regardless of the compatibility level Query processing feedback is a process by which the query processor in SQL Server Azure SQL Database and Azure SQL
OLAP systems involve processing complex aggregate queries on very large databases commonly called "data warehouses " Query response times can thus be very large for OLAP queries However since OLAP is an interactive process small query response times are required
Sample based approximate query processing AQP suffers from many pitfalls such as the inability to answer very selective queries and unreliable confidence intervals when sample sizes are small Recent research presented an intriguing solution of combining materialized pre computed aggregates with sampling for accurate and more reliable AQP We
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