The hash join is an example of a join algorithm and is used in the implementation of a relational database management system. All variants of hash join algorithms involve building hash tables from the tuples of one or both of the joined relations, and subsequently probing those tables so that only tuples with the same hash code need to be compared for equality in equijoins.
Hash joins are typically more efficient than nested loops joins, except when the probe side of the join is very small. They require an equijoin predicate (a predicate comparing records from one table with those from the other table using a conjunction of equality operators '=' on one or more columns).
The classic hash join algorithm for an inner join of two relations proceeds as follows:
First, prepare a hash table using the contents of one relation, ideally whichever one is smaller after applying local predicates. This relation is called the build side of the join. The hash table entries are mappings from the value of the (composite) join attribute to the remaining attributes of that row (whichever ones are needed).
Once the hash table is built, scan the other relation (the probe side). For each row of the probe relation, find the relevant rows from the build relation by looking in the hash table.
The first phase is usually called the "build" phase, while the second is called the "probe" phase. Similarly, the join relation on which the hash table is built is called the "build" input, whereas the other input is called the "probe" input.
This algorithm is simple, but it requires that the smaller join relation fits into memory, which is sometimes not the case. A simple approach to handling this situation proceeds as follows:
For each tuple in the build input
Add to the in-memory hash table
If the size of the hash table equals the maximum in-memory size:
Scan the probe input , and add matching join tuples to the output relation
Reset the hash table, and continue scanning the build input
Do a final scan of the probe input and add the resulting join tuples to the output relation
This is essentially the same as the block nested loop join algorithm.
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
A join clause in the Structured Query Language (SQL) combines columns from one or more tables into a new table. The operation corresponds to a join operation in relational algebra. Informally, a join stitches two tables and puts on the same row records with matching fields : INNER, LEFT OUTER, RIGHT OUTER, FULL OUTER and CROSS. To explain join types, the rest of this article uses the following tables: Department.DepartmentID is the primary key of the Department table, whereas Employee.DepartmentID is a foreign key.
Discrete mathematics is a discipline with applications to almost all areas of study. It provides a set of indispensable tools to computer science in particular. This course reviews (familiar) topics a
This hands-on course teaches the tools & methods used by data scientists, from researching solutions to scaling up
prototypes to Spark clusters. It exposes the students to the entire data science pipe
Ce cours est divisé en deux partie. La première partie présente le langage Python et les différences notables entre Python et C++ (utilisé dans le cours précédent ICC). La seconde partie est une intro
Organisé en deux parties, ce cours présente les bases théoriques et pratiques des systèmes d’information géographique, ne nécessitant pas de connaissances préalables en informatique. En suivant cette
Organisé en deux parties, ce cours présente les bases théoriques et pratiques des systèmes d’information géographique, ne nécessitant pas de connaissances préalables en informatique. En suivant cette
Organisé en deux parties, ce cours présente les bases théoriques et pratiques des systèmes d’information géographique, ne nécessitant pas de connaissances préalables en informatique. En suivant cette
Today we live in a digital society that requires the acquisition of new skills related to computer science, such as computational thinking or coding skills, as well as cross-curricular skills, such as communication, collaboration and creativity. A possible ...
Application of a single metal or alloy is often restricted by its properties from optimal combination of performance and cost. Therefore, there is a vast need of joining dissimilar metals for various applications in biomedical, aerospace, automobile and ma ...
Reducing CO2 emissions, restricting pesticides to protect health and biodiversity, enhancing corporate responsibility: why is Switzerland, one of the more democratic countries of the world, repeatedly failing to create a proper societal dialogue to face to ...