Sql queries on joins pdf

What is a linked server? Can you explain the types of Joins that we can have Explain how to store pdf file in sql server. Especially, the Schem- aRDD allows using the SQL queries, it also provides a more efficient way of working with data by utilizing its schema.

DataFrame still represents a distributed data collection but it does not inherit from RDD anymore. DataFrame also provides a domain-specific language. Running on cluster dataFramePeople. Such as RDDs, also DataFrames are lazy evaluated, so operations on DataFrame are executed when the result is required by invoking action.

Spark SQL offers a feature that extends its functionality by registering user-defined functions which may be used in SQL queries. User defined functions are registered via SQLContext.

Firstly, the terms that are used in this work are described. Every executor is run in separate process. Spark provides a possibility to run applications on a cluster via cluster managers such as Mesos and Yarn.

Apache Mesos is a cluster manager that abstracts CPU, memory, stor- age, and other compute resources away from machines physical or virtual , enabling fault-tolerant and elastic distributed systems to easily be built and run effectively [19].

Spark also provides its own built—in standalone cluster manager. So if you run your application from a local machine, the driver process is running on your local machine. This can cause an unwanted effect since a frequent transfer of data among local driver and workers may be slower than in cluster mode. A brain of the driver program is a SparkContext object that manages processes on a cluster. It also notifies executors that are created on the cluster nodes and sends them JAR in case Java and Scala containing the application code.

Finally it creates tasks for executors to be executed by them. On the worker nodes transformations are executed. Actions usually activate a transfer data from the worker nodes to the driver node, so if total amount of data on the worker nodes is larger than the available memory on the driver node it can be a problem that causes collapse of driver node.

Instead of adjusting the required setting parameters of SparkContext to run application according to them, the Spark submit script may be used. With the Spark submit script, the application packed in JAR is automatic- ally submitted to the worker nodes. Through SparkContext and by submit script, the resources such as memory or number of CPU cores of executors and driver may be scheduled. However just modes using cluster were described, the Spark engine provides a local mode too.

In local mode the Spark driver and executor run in the same Java process [7]. However there exist attempts to process XML documents by using relational databases.

Relational databases store non—structured data, so several approaches that map XML document to the relational table were invented. For this purpose both a schema map- ping and an order mapping can be used. Depending on the approach, with the schema mapping usually more than one table is created due to different structures of XML subtrees.

In this paper [21], there is described an al- gorithm for lossless schema mapping to generate a database schema from a DTD, which makes several improvements over existing algorithms. Also the other strategies for mapping XML to the relation table have been proposed by researchers such as [22] [23] or [24].

In this thesis we would like to process XML documents, so we need some mechanism that allows a transformation of an XML tree data model to the unordered relational data model.

Tree structure of XML document is ordered data model based on the document order, or more precisely said, it is based on the order of each element within XML document. Analysis and design of solution back to the valid and ordered XML. Accordingly we are not interested in the insertion or deletion of nodes. The transformation of XML document must fulfil a requirement of possibility to transform from the unordered relational model back to the XML document. This is accomplished by encoding order as a data value.

In the following sections three methods to the transformation of XML documents into the tables are presented and one of them will be chosen. Also the creation of the Edge table that includes order information as a result of the transformation, is described below.

The pre—order traversal function assigns num- ber to the root node then calls recursively the pre—order assignation to its child nodes from the left to the right. The result of this assignation is number representing an absolute position of node within XML document.

Figure 3. Note that the light—blue ellipses are element nodes and the light—green ellipses are text nodes. Global order encoding is the best on the queries evaluation, but not so good in the insertion of new nodes.

When a new node is inserted, the IDs of nodes following the new inserted ones must be actualized. Although it looks like that the floating—point values could solve renumbering problem, actually it could just partially.

In fact, integer and real are both stored by the same count of bits. The floating—point values could improve the performance of inserting, but in the worst case, when the count of inserted nodes is greater than available values nevertheless the renumbering must be performed.

For a better picture see Figure 3. It is easy to insert new node because just siblings following the new node have to be incremented. As it was mentioned in global order encoding, the float- ing—point values may also help, but with the same limitation. On the other hand, evaluation of queries, mainly following and preceding axis are diffi- cult to execute while there is no available global order information.

Edge table of local order encoding can be designed as Edge id, parentId, sIndex, pathId, value. Since the ID assigned to the node does not provide inform- ation about the position among its siblings, the sIndex as position must be added.

In this case ID is unique identifier that does not relate to the document order, so it is not assigned according to the document order. Analysis and design of solution 3. In dewey order encoding each node is not assigned just by a single number, but it is assigned by path.

The path represents a traverse from the root node of document to any concrete node of XML document. Each step of path represents a local order position information of ancestor node, but the complex path represents absolute node position within XML document. An evaluation of query within dewey encoding is very similar to the querying with global order encoding. Otherwise, inserting of new nodes cause that axis following—siblings and also their descendants must be updated.

Despite that the dewey order encoding uses advantages of two preceding methods, one disadvantage should be mentioned. If the XML tree is too deep, the dewey path may require more space to be stored while it is not a single value anymore. It must be stored as a vector or string. Seeing that Dewey path include enough information about the position in XML document, the Edge table may be defined as Edge dewey, pathId, value. Dewey order encoding is shown in the Figure 3.

As a consequence, a regular multi- line JSON file will most often fail [25]. From the three mentioned encodings the dewey order encoding will be used since it is the universal solution and the information stored in dewey path are sufficient.

In compare with the global encoding it can be a bit slower depending on the comparison of the paths. This thesis could be extended in a future by implementing a possibility to perform updates on really large XML files. Thus dewey encoding is the best option for our purposes. In the further sections we use terms edge table and nodes table in the same meaning.

Firstly we introduce a trivial method and then in the next sections it will be improved. The single methods are compared and we provide the results of local performance testing. The reason why we provide just local testing is that our early idea was to test functionality of Spark SQL API and find out whether it is usable to solve our problem. To ensure consistency of experiments each experiment was executed 10 times, whereby the first attempt was excluded since it was absolutely different from the others, and other measured times were averaged.

All local experiments were run on virtual machine hosted on an Intel Core i3 M 2. All experiments were run on Spark in version 1. A complex performance comparison of all methods is in the Chapter 6.

Information about tested tables containing transformed XML documents are in Table 5. For a faster local testing we were working with a small table of nodes containing 60 rows. The partial table of nodes can be found in Chapter 5. Our approach translations of simple XPath queries that covered all XPath axes.

It is an alternative to a document statement doc "xmlFile. Then, the inputted XPath query is translated step by step. After the translation of the last step, one more selection and filtration is needed. It completes the result of query by select- ing all descendant or self nodes of previously selected nodes. It is because the XPath steps traverse through the nodes, so by last extra step their content is appended. On our small testing file it was relatively fast to compute a result, but problems with performance began during the processing larger table of nodes containing rows.

After we examined an execution plan we found out, that for this naive method the Cartesian product followed by filtration was executed. Cartesian product makes a set of all pairs from the first and the second table. In this case the Cartesian product was bottleneck of the first method. The idea is to select nodes that are candidates for a next context node, then combine them with the current context node and based on the relation do filtration of suitable nodes from joined pairs by using user defined functions.

Note, that the context node is a set of nodes returned by executing one step of XPath query. We use this term in further chapters. Although the type of JOIN was defined, in some cases Spark has generated Cartesian product because joins conditions were not strong enough. Conditions were based on non—equality of dewey paths, and the user defined functions were used in a filter condition. Our approach defined function we add required UDF into the join condition.

It must be said that also a JOIN, whose condition is based only on the user defined function that require arguments from the both left and right table, invokes the Cartesian product because all pairs must be processed by UDF. By Table 4. Table 4. After twenty minutes of computing we were forced to cancel the third measurement marked with asterix. We realized that Cartesian product in Spark is really slow.

On a Spark cluster we did a test. We applied the Cartesian product on two tables with size 8MB and 9. After 24 hours less than one third of result was computed and Spark stored more than 60GB of data on disk. It compares the sizes of tables to be joined and broadcasts a smaller one across the workers. Alternative methods without joins 4. We had some ideas that essentially do not use joins.

To simplify the previous methods we wanted to select those nodes of some XPath step that are in desired relation with at least one node from the nodes of previously evaluated XPath step. This is the valid SQL query and both, the query and the nested query, are executable, but unfor- tunately Spark does not think the same. Our approach According to the physical plan Spark does not see the required, based on name filters that are important in this case and in addition, the filter shown in physical plan is always evaluated as true, so whole table is returned.

Also using two separated DataFrames on IN statement had no effect, while without a join Spark had no information about the nodes from the second table. Since there is no direct column to join two DataFrames we realized that instead of the join, a union of DataFrames could work for us. The idea was based on the union of the results of filtrations of two successively evaluated XPath steps.

But it again led to unexpected behavior of Spark that ignored WHERE condition and checked if each dewey path is the same dewey path it does not matter if it is author or book so condition was always fulfilled.

Broadcasted lookup collection than our all previous attempts. Semi means that the result contains just rows returned from one table. It means that if there exists a record in the right table that fulfill JOIN ON condition, just a record from the left table is returned. By this method we implemented a translation of XPath steps only for parent, child, ancestor, ancestor-and-self, descendant and descendant-or- self axes.

It is because the implemented axes are based on prefixes. The Table 4. Hence we decided to not continue on development of this method and we rather wanted to improve the new solution. The method that in a principle does not use the joins is described in Chapter 4. This table is often consider as a lookup table.

Our approach Since when we know that it is impossible to work with two Data- Frames in the same time without joining them together, we had to find out how to deal with this limitation. We adapt the idea of lookup table, but since we had bad experience with the joins, we wanted to avoid them.

Our idea of avoidance of the JOIN clause is creation of a collection from the context node by applying collect action on the DataFrame. Firstly, the action collect creates a collection of Strings where each element is dewey path.

Then we register a user defined function, and during the registration, the broadcast variable from the collection is created. Input parameter of the user defined function is a dewey path of candidate for a member of new context node.

The candidates for a new context node are all rows whose value of column value fulfill the node test of XPath step. Called UDF checks whether the relationship between inputted dewey path and the dewey paths in the collection of context node is as it is desired. If UDF is evaluated as true, a currently checked node will be member of next lookup table. Advantage of this method is that each executor may have its own partitions of input file in memory and just lookup collections are collected to the driver and then broadcasted among other executors.

The user defined functions used in this method are different from those that are used in the Pure SQL method mentioned in Chapter 4. We created UDF separately for each axis. The difference is that these functions firstly create broadcast variable and then, according to the axis specifier they detect whether examined node belongs to the axis that was desired.

Instead of two input parameters, just one is required by UDFs in this method, seeing that they use the broadcast variable. Principle of UDF relation checking is described in Chapter 5. Also in this method the evaluation starts with a selection of parent node of root node and then, the independent XPath steps are evaluated step by step. By the evaluation of the last XPath step the result nodes are gotten, but they still do not contain their content such as text nodes or other descendant elements.

The last step of the evaluation that returns nodes that are the correct result of XPath query is described in Chapter 4. Unlike the previous measurement in Chapter 4. Getting result Table 4. We have just nodes that do not contain their nested — descendant nodes. Methods mentioned in this chapter cannot be used for this purpose since they traverse just through the element nodes.

Their problem is that their results do not contain text nodes and they return distinct results. In the final we want to have a set of the result nodes, so the fact must be con- sidered that the absolute evaluation of some axes can return duplicates.

It is because the result is set that contains all result nodes and their nested nodes. Mostly, the computation of the final result took more than the evaluation of XPath steps. We finally create a user defined function and via the UDF one more column is added to the DataFrame that is being processed. The new column contains a number that indicates how many times current node is in the result set. Then the flatMap transformation, that returns RDD containing duplicates according to the number in the last column, is invoked.

RDD is then easily converted back to the DataFrame. Our approach 4. We have started with the trivial method that uses the Cartesian product to join the single XPath steps.

From our measurements we can see that Cartesian product is usable just with the small tables. By analysis of the physical plans of our methods we were able to improve them. We also designed multiple methods, but Spark showed its limitations and some of them could not be realized. Finally we designed the method that uses the one of the biggest advantage of Spark called broadcast variables. As it is shown in our measurements it is the fastest method from the all that we have introduced.

First, two tables are joined, then the third table is joined to the result of the previous joining. Back to articles list Articles Cookbook. Tags: cheat sheet joins. Rock the SQL! Do you need to combine two tables without a common column?

Do you only use equals in your JOIN conditions? Learn how non-equi JOINs can assist with listing duplicates and joining tables based on a range of values.

Have you ever wondered how to join three tables in SQL? Leave a Reply Cancel reply Your email address will not be published. It is an keyword in SQL that is used to rename a column or table using an alias name. It is a function that takes the name of a column as argument and counts the number of rows when the column is not NULL. It is used to create a new table in a database and specify the name of the table and columns inside it. It is a function that takes number of columns as an argument and return the largest value among them.

It is a function that takes number of columns as an argument and return the smallest value among them. It is an operator that is used to filter the result set to contain only the rows where either condition is TRUE.