synapse | Synapse : Matrix homeserver written in Python/Twisted | Math library

 by   matrix-org Python Version: v1.86.0rc2 License: Apache-2.0

kandi X-RAY | synapse Summary

kandi X-RAY | synapse Summary

synapse is a Python library typically used in Utilities, Math applications. synapse has no bugs, it has no vulnerabilities, it has a Permissive License and it has high support. However synapse build file is not available. You can install using 'pip install synapse' or download it from GitHub, PyPI.

Synapse: Matrix homeserver written in Python/Twisted.

            kandi-support Support

              synapse has a highly active ecosystem.
              It has 10951 star(s) with 2033 fork(s). There are 247 watchers for this library.
              There were 10 major release(s) in the last 12 months.
              There are 1385 open issues and 4842 have been closed. On average issues are closed in 190 days. There are 65 open pull requests and 0 closed requests.
              It has a positive sentiment in the developer community.
              The latest version of synapse is v1.86.0rc2

            kandi-Quality Quality

              synapse has 0 bugs and 0 code smells.

            kandi-Security Security

              synapse has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              synapse code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              synapse is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              synapse releases are available to install and integrate.
              Deployable package is available in PyPI.
              synapse has no build file. You will be need to create the build yourself to build the component from source.

            Top functions reviewed by kandi - BETA

            kandi has reviewed synapse and discovered the below as its top functions. This is intended to give you an instant insight into synapse implemented functionality, and help decide if they suit your requirements.
            • Updates the membership of a room .
            • Performs a synchronous search on a user .
            • Creates a new room .
            • Add the chain cover index .
            • Sends a request to the given destination .
            • Persist client event .
            • Gets backfills from the room .
            • Method to prune events from a table .
            • Attempts to upgrade an existing database .
            • Get a list of rooms changed .
            Get all kandi verified functions for this library.

            synapse Key Features

            No Key Features are available at this moment for synapse.

            synapse Examples and Code Snippets

            Install twisted under CentOS6
            Pythondot img1Lines of Code : 6dot img1License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            yum install libbz2
            yum install libbz2-devel
            yum install bzip2
            yum install bzip2-devel

            Community Discussions


            AttributeError: 'DataFrame' object has no attribute 'randomSplit'
            Asked 2022-Mar-17 at 11:49

            I am trying to split my data into train and test sets. The data is a Koalas dataframe. However, when I run the below code I am getting the error:



            Answered 2022-Mar-17 at 11:46

            I'm afraid that, at the time of this question, Pyspark's randomSplit does not have an equivalent in Koalas yet.

            One trick you can use is to transform the Koalas dataframe into a Spark dataframe, use randomSplit and convert the two subsets to Koalas back again.



            Access issue with Azure Synapse studio
            Asked 2022-Mar-15 at 15:39

            I created a Synapse workspace in my Azure Portal and tried opening the Synapse studio and I received the following error:

            Failed to load one or more resources due to No access, error code 403.



            Answered 2021-Sep-06 at 19:58

            Go to your storage account -> Access Control (IAM) -> Role Assigments and check if you can find ther role storage-blob-data-contributor if not add it.

            This role shoulde be added automaticly but there are exceptions fron this rule

            Detials are here how-to-grant-workspace-managed-identity-permissions



            IF and Switch activities not available in the pipeline Activities pane
            Asked 2022-Mar-04 at 12:12

            Using Synapse ADF, IF and Switch activities are not available in the pipeline Activities pane

            Microsoft says they should be there If Condition activity - Azure Data Factory & Azure Synapse

            Is the list of activities configurable ? How to add activities ?



            Answered 2022-Mar-04 at 12:12

            You cannot nest these activities so If and Switch will not appear on the menu when you are inside an If or a Switch. You also cannot nest For Each activities. Simply come back up to the top level of your pipeline and you will see the activities.

            If you have more complex logic, think about using the logical functions in the expression language like and and or, or using like a Stored Procedure, Databricks Notebook, Azure Synapse Notebook, etc



            WSO2 API Manager(wso2am-4.0.0) - Where are the synapse configurations located in the latest version?
            Asked 2022-Mar-01 at 18:20

            I'm currently using wso2am-4.0.0 and I have been using wso2am-2.1.0 previously.

            In the previous version, a synapse configuration xml per each API could be located in the below folder path.



            Answered 2022-Mar-01 at 18:20


            Data Factory, Synapse Analytics and DataBricks Comparison
            Asked 2022-Feb-26 at 10:50

            I'm little bit new on Azure and I'm wondering when is recommendable to use ADF, Synapse, or DataBricks. What are their use cases for best practices and performance?

            Could you help me with this theorical question?




            Answered 2022-Feb-25 at 11:51

            The straight-forward answer to your question is they all are ETL/ELT and Data Analytics tool with some different approach and features.

            When comes to Azure Data Factory vs Synapse, they both are almost same except some features. When building an analytics solution in Azure, we recommend starting with Synapse since you have a fully integrated design experience and Azure analytics product conformance in a single pane of glass. Azure Data Factory used for Migration databases and copy files. You can find most differences between these two services here: Differences from Azure Data Factory - Azure Synapse Analytics

            Azure Data Factory vs Databricks: Key Differences

            Azure Data Factory vs Databricks: Purpose

            ADF is primarily used for Data Integration services to perform ETL processes and orchestrate data movements at scale. In contrast, Databricks provides a collaborative platform for Data Engineers and Data Scientists to perform ETL as well as build Machine Learning models under a single platform.

            Azure Data Factory vs Databricks: Ease of Usage

            Databricks uses Python, Spark, R, Java, or SQL for performing Data Engineering and Data Science activities using notebooks. However, ADF provides a drag-and-drop feature to create and maintain Data Pipelines visually. It consists of Graphical User Interface (GUI) tools that allow delivering applications at a higher rate.

            Azure Data Factory vs Databricks: Flexibility in Coding

            Although ADF facilitates the ETL pipeline process using GUI tools, developers have less flexibility as they cannot modify backend code. Conversely, Databricks implements a programmatic approach that provides the flexibility of fine-tuning codes to optimize performance.

            Azure Data Factory vs Databricks: Data Processing

            Businesses often do Batch or Stream processing when working with a large volume of data. While batch deals with bulk data, streaming deals with either live (real-time) or archive data (less than twelve hours) based on the applications. ADF and Databricks support both batch and streaming options, but ADF does not support live streaming. On the other hand, Databricks supports both live and archive streaming options through Spark API.

            Azure Synapse vs Databricks: Critical Differences

            Azure Synapse vs Databricks: Data Processing

            Apache Spark powers both Synapse and Databricks. While the former has an open-source Spark version with built-in support for .NET applications, the latter has an optimized version of Spark offering 50 times increased performance. With optimized Apache Spark support, Databricks allows users to select GPU-enabled clusters that do faster data processing and have higher data concurrency.

            Azure Synapse vs Databricks: Smart Notebooks

            Azure Synapse and Databricks support Notebooks that help developers to perform quick experiments. Synapse provides co-authoring of a notebook with a condition where one person has to save the notebook before the other person observes the changes. It does not have automated version control. However, Databricks Notebooks support real-time co-authoring along with automated version control.

            Azure Synapse vs Databricks: Developer Experience

            Developers get Spark environment only through Synapse Studio and do not support any other local IDE (Integrated Development Environment). It also lacks Git integration with Synapse Studio Notebooks. Databricks, on the other hand, enhances developer experience with Databricks UI, and Databricks Connect that remotely connects via Visual Studio or Pycharm within Databricks.

            Azure Synapse vs Databricks: Architecture

            Azure Synapse architecture comprises the Storage, Processing, and Visualization layers. The Storage layer uses Azure Data Lake Storage, while the Visualization layer uses Power BI. It also has a traditional SQL engine and a Spark engine for Business Intelligence and Big Data Processing applications. In contrast, Databricks architecture is not entirely a Data Warehouse. It accompanies a LakeHouse architecture that combines the best elements of Data Lakes and Data Warehouses for metadata management and data governance.




            Load 600+ million records in Synapse Dedicated Pool with Oracle as Source
            Asked 2022-Feb-20 at 23:13

            I am trying to do a full load a very huge table (600+ million records) which resides in an Oracle On-Prem database. My destination is Azure Synapse Dedicated Pool.

            I have already tried following:

            Using ADF Copy activity with Source Partitioning, as source table is having 22 partitions

            I increased the Copy Parallelism and DIU to a very high level

            Still, I am able to fetch only 150 million records in 3 hrs whereas the ask is to complete the full load in around 2 hrs as the source would be freezed to users during that time frame so that Synapse can copy the data

            How a full copy of data can be done from Oracle to Synapse in that time frame?

            For a change, I tried loading data from Oracle to ADLS Gen 2, but its slow as well



            Answered 2022-Feb-20 at 23:13

            There are a number of factors to consider here. Some ideas:

            • how fast can the table be read? What indexing / materialized views are in place? Is there any contention at the database level to rule out?
            • Recommendation: ensure database is set up for fast read on the table you are exporting
            • as you are on-premises, what is the local network card setup and throughput?
            • Recommendation: ensure local network setup is as fast as possible
            • as you are on-premises, you must be using a Self-hosted Integration Runtime (SHIR). What is the spec of this machine? eg 8GB RAM, SSD for spooling etc as per the minimum specification. Where is this located? eg 'near' the datasource (in the same on-premises network) or in the cloud. It is possible to scale out SHIRs by having up to four nodes but you should ensure via the metrics available to you that this is a bottleneck before scaling out.
            • Recommendation: consider locating the SHIR 'close' to the datasource (ie in the same network)
            • is the SHIR software version up-to-date? This gets updated occasionally so it's good practice to keep it updated.
            • Recommendation: keep the SHIR software up-to-date
            • do you have Express Route or going across the internet? ER would probably be faster
            • Recommendation: consider Express Route. Alternately consider Data Box for a large one-off export.
            • you should almost certainly land directly to ADLS Gen 2 or blob storage. Going straight into the database could result in contention there and you are dealing with Synapse concepts such as transaction logging, DWU, resource class and queuing contention among others. View the metrics for the storage in the Azure portal to determine it is under stress. If it is under stress (which I think unlikely), consider multiple storage accounts
            • Recommendation: load data to ADLS2. Although this might seem like an extra step, it provides a recovery point and avoids contention issues by attempting to do the extract and load all at the same time. I would only load directly to the database if you can prove it goes faster and you definitely don't need the recovery point
            • what format are you landing in the lake? Converting to parquet is quite compute intensive for example. Landing to the lake does leave an audit trail and give you a position to recover from if things go wrong
            • Recommendation: use parquet for a compressed format. You may need to optimise the file size.
            • ultimately the best thing to do would be one big bulk load (say taking the weekend) and then do incremental upserts using a CDC mechanism. This would allow you to meet your 2 hour window.
            • Recommendation: consider a one-off big bulk load and CDC / incremental loads to stay within the timeline

            In summary, it's probably your network but you have a lot of investigation to do first, and then a number of options I've listed above to work through.



            ARM template - bad request failed when deploying a linked service
            Asked 2022-Feb-19 at 14:32

            I am trying to deploy a synapse instance via an ARM template and the deployment is successful via the Azure DevOps portal, but when I try to deploy the same template with an Azure Keyvault linked service I encounter the following error:



            Answered 2022-Feb-03 at 19:44

            I had to contact Microsoft support and their reply was the following:

            ARM templates cannot be used to create a linked service. This is due to the fact that linked services are not ARM resources, for examples, synapse workspaces, storage account, virtual networks, etc. Instead, a linked service is classified as an artifact. To still complete the task at hand, you will need to use the Synapse REST API or PowerShell. Below is the link that provides guidance on how to use the API.

            This limitation in ARM is applied only to Synapse and they might fix this in the future.

            Additional references:





            Azure synapse deployment failing
            Asked 2022-Feb-17 at 17:50

            I am trying to deploy SQL files to an Azure Synapse Analytics dedicated SQL pools using PowerShell script in an Azure Devops pipeline.

            I have a folder with SQL files and after defining array of files I am trying to run foreach loop for array and trying to Invoke-Sqlcmd to deploy files but first SQL file get deployed (object is created) and then I get error:

            Msg 104309, Level 16, State 1, Line 1 There are no batches in the input script.

            Below is my piece of code:



            Answered 2022-Feb-17 at 17:50

            Azure Synapse Analytics dedicated SQL pools scripts are sensitive to empty batches, eg this script would generate the same error:

            Msg 104309, Level 16, State 1, Line 1 There are no batches in the input script.



            ESB WSO2 Send a message to Postman (using Enrich Mediator?)
            Asked 2022-Feb-15 at 10:28

            I Created a MySQL Database and I want to sent some information to postman after a request.

            This is my code:



            Answered 2022-Feb-15 at 10:28

            Currently, after the dblookup the query result is your message. So if you respond after the log statement for example but skip the enrich, you should send those variables back to the client.

            You could also use a PayloadFactory or XSLT transformation to transform the message if you want. But the data is already there, you actually lose it right now due to the enrich.



            How to Read Synapse Analytics SQL Script (JSON Format) as a SQL Script in an IDE?
            Asked 2022-Feb-11 at 14:39

            I have a Synapse Git Project that has SQL Scripts created in the Azure Portal like so Microsoft Docs SQL Scriptand the challenge is that in GIT they appear as this kinda bulky JSON file and I would love to read it as SQL File DBEAVER or IntelliJ …

            Any way to do this without having to manually select the Query Section of the file and kinda clean it?



            Answered 2022-Feb-11 at 14:39

            First Some Background

            Synapse stores all artifacts in JSON format. In the Git repo, they are in the following folder structure:

            Inside each folder are the JSON files that define the artifacts. Folder sqlscript contains the JSON for SQL Scripts in the following format:

            NOTE: the Synapse folder of the script is just a property - this is why all SQL Script names have to be unique across the entire workspace.

            Extracting the script

            The workspace does allow you to Export SQL to a .sql file:

            There are drawbacks: you have to do it manually, 1 file at a time, and you cannot control the output location or SQL file name.

            To pull the SQL back out of the JSON, you have to access the properties.content.query property value and save it as a .sql file. As far as I know, there is no built in feature to automatically save a Script as SQL. Simple Copy/Paste doesn't really work because of the \ns.

            I think you could automate at least part of this with an Azure DevOps Pipeline (or a GitHub Action). You might need to copy the JSON file out to another location, and then have a process (Data Factory, Azure Function, Logic App, etc.) read the file and extract the query.


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install synapse

            You can install using 'pip install synapse' or download it from GitHub, PyPI.
            You can use synapse like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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