Greenplum software

On the other hand, the PostgreSQL database tool employs the client-server architecture. This means that the database architecture can share the memory, storage area, and operating systems. This is done so it has shared memory and processes can work together and enables the building of an instance that facilitates access to data. Then, the instance created is relevant in their connection to the client programs for reading and writing operations in the physical servers.

The difference in their architecture gives out the differences in the mode of processing. The Greenplum handles the parallel processing elements in the system because of its complexity and robust nature and the PostgreSQL ability to perform single processing. On the contrary, PostgreSQL is a database tool that has an advantage over query planning in addition to the legacy query planner.

This distinguishes it from the Greenplum database tool. Most importantly, the Greenplum database tool has the option of the utilization of column storage. This data store has been logically organized in a table, for instance, rows and columns. This categorizes Greenplum as a database tool that has provisions for compression features on all the append-optimized tables used in the column storage in relational database systems.

The architecture of PostgreSQL makes it easy for their modification and supplementation to provide support for the parallel structure of the Greenplum database tool.

The Greenplum database feature called the interconnect allows for communication over high speed network protocols between the distinct PostgreSQL instances created, making them behave in one logical way and viewed as one database image.

Greenplum can be optimized to handle large data sets as opposed to PostgreSQL. The Greenplum dataset tool is essential in physical servers and can use declarative partitions and sub partitions to enable the generation of partition constraints.

Despite the differences in architecture and features, both the Greenplum and PostgreSQL are viewed as related database tools. The functionality of both the database tools in parallel and single processing of computation problems and data analytics provides a preference for which tool to use.

Both the tools are best applied in the computation environment depending on the nature of the task. Click and Get More Information! We wrote a book about Greenplum! Read it to understand how to bring the most out of your Greenplum capabilities. Skip to content.

Latest commit. Git stats 38 commits. Failed to load latest commit information. View code. Summary Prerequisites Deploy and test the nifi processor Build and development. Summary This software is intended to be a simple non production ready processor for apache nifi server, using Greenplum Streaming Service functionalities.

About No description, website, or topics provided. Releases No releases published. Packages 0 No packages published. You signed in with another tab or window. For example, figure e in the example. And if the write and write to the same table simultaneously, and to the same tuple, it will be processed according to the isolation level. Indexing can speed up the reading and writing of tuples. The index finds the physical location where the tuple is stored by value, and reads the tuple from that location.

Compared with scanning, indexing is not faster than sequential scanning every time. So, when to use sequential scans and when to use indexes, this is what the query optimizer needs to do for us. In addition, how to maintain the index structure? What types of indexes are there? How to control concurrency? Is indexing necessarily better than scanning?

Now, the addition, deletion, checking and modification of tuples can be supported, but this is far from enough. In order to facilitate the use of users and provide more powerful and useful functions, Greenplum provides an execution engine.

When the query is executed, the SQL statement passes through the parser, and the string SQL statement is parsed into a structured tree, the most effective execution plan is made through the optimizer, and the executor executes the query result. The SQL statement in the following example connects two tables.

In Greenplum, the executor executes the query plan through iterators, that is, from top to bottom, one tuple at a time. Each executor node provides the next tuple up and gets the next tuple down. There may be multiple query plans for a statement, such as the sequential scan and index scan mentioned earlier.

The query optimizer will be smart enough to choose the least expensive execution plan. In the example, the bottom is the scan operation on the two tables. After the scan is over, in order to perform Join, the Bars table needs to be redistributed according to the name of the tuple, so that the Bars tuple and Sells tuple with connection conditions can be gathered Together.

Since Sells are already distributed according to bars, there is no need to redistribute Sells here. Finally, after finishing the projection operation, the results need to be aggregated to the QD node, which is done by Gather Motion at the top level.

If the index scan is adopted, one of the problems is that the tuples executed by the index are in the file, resulting in random access to the disk. One solution is Greenplum Cluster operation. The Cluster operation reorders the tuples in the file. Another solution, especially if there are multiple conditions, can be scanned based on bitmaps. The bitmap information of each condition can be obtained according to the query conditions.

The bitmap records which tuples meet the query conditions and satisfy the query conditions. The tuple of is represented by 1 in the bitmap. The bitmap corresponding to the two query conditions is used to obtain the final bitmap to be scanned through the bitmap AND operation.

Based on bitmap scanning, files can be accessed in sequential access mode. The first one is Nested Loop Join, which is similar to the file storage mentioned earlier, that is, two loops are superimposed to match the scans inside and outside, and the result is returned. A possible variant here is that the inner loop may use indexes instead of sequential scans to make execution more efficient.

The second is Merge Join. Merge Join has two stages. The first stage is to sort the tuples to be connected according to the connection conditions. Then in the second stage, the merge operation is performed based on the sorted tuples. The third way is hash join. In hash join, one table is generally used as a lookup table and another small table as a hash table.

If the hash table is small enough, it can be stored in memory. During each search, the lookup table is scanned to see whether the current tuple has a match in the hash table.

If there is a match, it will be returned directly, otherwise it will be skipped. But the problem is what if the hash table is too large? Which tuples need to be stored in external storage? How to deal with the hash table tuples that need to be matched in the outer memory? If the Greenplum process hangs in the process of modifying files, how to ensure data consistency? A classic problem often mentioned in database courses is: transfer from account A to account B.

If the system restarts and crashes after account A decreases by , will it happen that account A decreases by but account B does not increase by ? Greenplum guarantees the atomicity of operations through transactions. Another problem is the isolation between transactions.

If the transfer transaction and the interest rate increase transaction are carried out at the same time, if the operation is carried out in the wrong sequence in the figure, there will be problems, and in the end you will find that 2 dollar is missing! Using transaction isolation can solve this type of headache.

Every time data is written, the memory is changed first, and then the disk is written. In the following example, A is first modified to 23 in memory. After submission, if the system hangs, the modification of A will be lost. When restarting, you will find that A is still equal to 12, because the modification has not been written yet.

Back to the disk, Of course, requiring every modification to write to the disk can prevent such problems from happening, but there will be efficiency problems.

The log function provided by Greenplum can solve such problems. The log records the modification process of the database in detail. Log records are accessed sequentially and provide the concept of a logical timeline. The efficiency is much higher than that of random access to the disk.