Filter to only show Relevant Dimension Values
Morning Sisense community! I have my Fact Table and a related Dimension Table. My dimension table contains more unique values than are actually present in the fact table. When I use a field from the Dimension Table as a filter, the filter displays all values from the dimension, even those that have no corresponding fact records. I only want the filter to show dimension values that are relevant to the data in the fact table. Example Tables: fact_table dimension_table id dim_id dim_id dim_name 1 1 1 one 2 2 2 two 3 1 3 three 4 2 4 four fact_table and dimension_table have a relationship in the EC based on dim_id Desired Outcome: In the filter based on dim_name, I only want to see "one" and "two", but currently, I see all four: "one," "two," "three," and "four." How do I achieve my desired outcome?
Dashboard not auto-refreshing for mobile app performance data
Hi everyone, I’m using Sisense to track performance metrics from my mobile entertainment app — like daily active users, watch duration, and session time. Lately, I’ve noticed that some dashboards don’t refresh automatically, and I have to manually reload them to see updated results. I’ve already checked the data connector and refresh schedule, but everything looks fine there. Could this delay be due to caching, or maybe a dashboard setting I’m missing? Would appreciate any suggestions or settings to look into — thanks in advance!How To Troubleshoot Build Failures (Linux OS)
HOW TO TROUBLESHOOT BUILD FAILURES (Linux OS) Building an ElastiCube imports the data from the data source(s) that have been added. The data is stored on the Sisense instance, where future dashboard queries will be run against it. You must build an ElastiCube at least once before the ElastiCube data can be used in a dashboard. This article will help you understand and troubleshoot common build failures and covers the following: Steps of the Build Process Troubleshooting Tools Troubleshooting Techniques Common Errors & Resolutions Please note the following was written for Sisense on Linux as version L2022.5. Steps of the Build Process 1. Initialization: The initialization stage of the build process prepares the platform for the data import, which includes checking and deploying the resources geared towards performing the build. 2. Table Import: This step imports the data from the external data platforms into the ElastiCube. The ec-bld Pod runs two to three concurrent containers, meaning that two to three pods can be processed simultaneously. The build pod, which uses the given connector frameworks (old or new, based on the connector used), connects to the given source(s). By default, 100,000 lines of data are read and imported per cycle during this phase. The MServer is responsible for getting the data from the connectors and writing it to storage into Sisense’s database (MonetDB). While importing the data, the process uses the given query assigned to the given data source (either the default Select All or a custom query). 3. Custom Table Columns: This step of the build process runs the data enrichment logic defined in the ElastiCube modeling. There are three types of custom elements: Custom Columns (Expressions) Custom Tables (SQL) Custom Code Tables (Python-based Jupyter Notebooks) Custom Elements uses the data previously imported during the Base Tables phase as its input. The calculations/data transformation happens sequentially one after the other based on the Build Plan/Dependencies generated earlier in the process between finalizing the Initialization phase and at the starting of the Base Tables phase. Calculations occur locally based on the data in the ElastiCube, and can consume lots of CPU and RAM based on the complexity of the Expressions/SQL/Python Jupyter Notebooks. 4. Finalization: These steps in the process finalize the ElastiCube’s build and readies it for use. The steps include: I. The current (up-to-date) data of the ElastiCube is written to a disk. II. The management pod stops the current ElastiCube running in Build Mode (ec-bld Pod, and its ReplicaSet + Deployment controllers). III. The management pod creates a new ElastiCube running in Query Mode (ec-qry Pod, and its ReplicaSet + Deployment controllers). IV. Once the new ElastiCube is ready, it becomes active and available to answer data queries (e.g., dashboard requests). V. The management pod stops the previous ElastiCube running in Query Mode (ec-qry Pod, and its ReplicaSet + Deployment controllers). Builds may be impacted by several factors. It is recommended to test your build process and tune accordingly when changes are made to the following: Hardware Sisense architecture Middleware Data source Connectivity, networking, and security policies Sisense upgrade/migration from Windows to Linux Sisense configuration Increase in data volume Data model schema (i.e., number and complexity of custom tables and import queries) Troubleshooting Tools Leverage the following when troubleshooting build issues: Inspect log files Each log contains information related to a different part of the build process and can help identify the root cause of your build issue. Depending on your Sisense deployment, logs may be located in different directories. The default path for Single Node is /var/log/Sisense/Sisense. For Multi Node, it’s on the application node inside the Management pod. If you need to collect logs, make sure to do so soon after the build failure, as logs will be trimmed after they reach a certain size. Log name Description Build.log General build logs will contain information for all the Elasticubes. Query.log General query logs will contain information for all the queries. Management.log Elaborate log file, which contains service communication requests. (Build will reach out to Management to fetch info from MongoDB etc.) Connector.log General information for all builds and connectors. Translation.log All the logs related to the translation service. ec-<cube name>-bld-<...>.log This contains the latest build log for each cube. It can also be viewed through the UI, as shown here. ec-<cube name>-qry-<...>.log Contain logs related to specific Elasticubes’ queries. build-con-<cube name>-<...>.log More verbose logs provide connector-related details for specific builds. Combined.log Aggregation of all logs in one file. It can be downloaded via the Sisense UI, as shown here. Please note if you are a Managed Services customer, only the combined log and latest build log for each cube are available. Use Grafana to check System Resources Grafana is a tool that comes packaged with Sisense that can be used to monitor system resources across pods. Every build has its own pod. This allows you to see the CPU and RAM that each build uses, as well as what is used by your whole Sisense instance. Excessive CPU and RAM usage is a common cause of build failures. 1. Go to Admin > System Management > Click on Monitoring. Click on the Search icon and then select All pods per namespace and then select namespace where Sisense is deployed (by default is “sisense”). 2. In the Pod dropdown, search for “bld” and select the cube you want to observe. *You may need to reduce the timeframe to get results: 3. Observe CPU and RAM over the duration of the build. *In the CPU graph, 1 core is represented by 100% See this article for additional information on using Grafana. Use Usage Analytics to observe build metrics Usage Analytics contains build data and pre-built dashboards to assist you in identifying build issues and build performance across cubes over time. See here for documentation on this feature. Ensure you have usage analytics turned on and configured to keep the desired history! Troubleshooting Techniques Below are some common issues and suggestions for build errors. The first step is to read and understand the error message on the Sisense portal. This will help resolve the exact build issue. 1. Whenever you face build issues, check the Memory consumption. Options include either ssh to your Linux machine and run “top” command to check the process and memory consumption, or you can also open grafana/logz.io and check memory consumption by the pod. If you see high memory usage, then please try to schedule builds in the off hours to see if that helps. 2. If the cube is too big, try to break the cube into multiple cubes by sharding the data or separating use cases. 3. Check the data groups first to see if one specific cube is very large or if you only have a default data group. If all the cubes are part of that data group, then create a different group for the large cube. 4. If the error message is related to Safe Mode (“Your build was aborted due to abnormal memory consumption (safe mode)”), then check the Max RAM value set in the data groups. You can increase the Max RAM value and verify the build. (https://support.sisense.com/kb/en/article/how-to-fix-safe-mode-on-build-dashboard) See the following two articles for details on managing data groups: https://documentation.sisense.com/docs/creating-data-groups https://community.sisense.com/t5/knowledge/how-to-configure-data-groups/ta-p/2142 5. Running concurrent build processes can also be an issue. Try to not run multiple builds at the same time. If that is the issue, then open the Configuration Manager (/app/configuration), expand the Build section, Change the value of Base Tables, Max Threads to 1 and save. (Relevant pod should restart automatically, but you can also restart the build Pod manually using “kubectl -n sisense delete pod -l app=build” 6. Lack of sufficient storage space to create a new ElastiCube (either in Full or Accumulative build) can also result in build failure. It is recommended to free up some space and then check the build. 7. Check the log files and the query running in the backend to try to break down complex queries to avoid memory consumption. 8. The below items outline the configurations that affect troubleshooting: -Base Tables Max Threads: Limits the number of Base Tables that are built simultaneously in the SAME ElastiCube -Timeout for Base Table: Will probably “forcefully” fail the build if any Base Table takes more than this amount of time to build, available via the “Build” configuration Remember that making any changes to these settings might require pod restart: To restart the pod, run the following command: kubectl -n sisense delete pod -l app=build Check the pod restarted based on the pod age: Kubectl -n sisense get pods -l app=build 9. If you have many custom tables, try to use the import query (move the custom table query into the custom import query). Documentation: Importing Data with Custom Queries - Introduction to Data Sources 10. Please check your data model design and confirm that it conforms to Sisense best practices. For example, M2M takes more memory and can result in build failures. https://support.sisense.com/kb/en/article/data-relationships-introduction-to-a-many-to-many 11. Builds can also fail because of the network connection between data sources and the Sisense server. Perform a Telnet test to verify connectivity from the Sisense server to the data server. Common Build Errors and Resolutions Error Description Resolution BE#468714 Management response: ElastiCube failed to start - failed to detect the EC; Failed to create ElastiCube for build process. This means the process does not have enough resources to bring up the build pod. If the Kubernetes process is still running for creating the Pod, the following command will allow you to monitor the given Build pods being brought up and check once they are healthy, up, and running. Command: kubectl -n sisense get pods -l mode=build -w If the value for that pod in the restarts column is greater than 0, it means that the Pod is not able to be initialized properly and will retry 5 times until it fails and terminates the process. If the build process had already terminated in the past, view the Kubenetes journal to find out the reason for failure. Command: sudo journalctl --since=” <how long ago>” | grep -i oom For example, if the build occurred within the past hour or so, a “50M” ago and grep on “oom” will show if an out of memory issue occurred for the given build. Example: sudo journalctl --since=” 50M ago” | grep -i oom, which would indicate an oom_kill_process was put into place due to out-of-memory reasons. BE#196278 failed to get sessionId for dataSourceId This error indicates that the user running the build does not have permission to run the build for the given ElastiCube. The ElastiCube needs to be shared with the user with “Model Edit” permission. BE#470688 The reason for this issue is a cumulative build is being performed, which relies on having the ElastiCube stored in the farm fails because either access to the directory in the farm storage location or directory/files are corrupted or are not there. The only way to resolve it is to either restore the farm directory from a backup for the ElastiCube or re-build the ElastiCube with a full build. BE#313753 Circular dependency detected This happens when you have a lot of custom tables and custom columns which depend on each other Please check the below articles on how to avoid loops: https://documentation.sisense.com/docs/handling-relationship-cycles#gsc.tab=0 Error: Failed to read row:xxxxxxx, connector Sisense is importing data from the database using a Generic JDBC connector. Why did this fail suddenly? The data added recently is not in the correct format or as expected in the table. If you are using a Generic JDBC connector, then it’s worth checking the connector errors online where you may find useful information to resolve the issue related to the connector. BE#640720 Build failed: base table <table name> was not completed as expected. Failed to read row: 0, Connector (SQL compilation error: invalid number of result columns for set operator input branches, expected 46, got 45 in branch 2). Most likely issue in the custom import query or on the target table. Please check if there are right amount of columns used in the query and refresh table schema. Build failed: BE#636134 Task not completed as expected: Table TABLE_NAME : copy_into_base_table build Error -6: Exception for table TABLE_COLUMN_NAME in column COLUMN_NAME at row ROW_NUMBER: count X is larger than capacity Y This could be resolved by changing BaseTableMax (parallel table imports) from 4 to 1 in the Configuration Manager. Conclusion Understanding the exact error message is the first step towards resolution. Based on the symptom you can try some of the suggestions listed above and can quickly resolve build failure issues. If you need any additional help, please contact Sisense Support or create a Support Case with all the log files listed above, and a Support Engineer will be able to assist you. Remember to include all relevant log files for an efficient troubleshooting process! Krutika Lingarkar, Technical Account Manager in Customer Success, wrote this article in collaboration with Chad Solomon, Technical Account Manager, Senior in Customer Success, and Eran Ganot, Tech Enablement Lead in Field Engineering.
Choosing the Right Data Model
This post has become outdated. You can find guidance on choosing a data model on our documentation site here. https://docs.sisense.com/main/SisenseLinux/choosing-the-right-data-model.htm Introduction Customers often run into the question of which data model they should use (an ElastiCube, a Live model, or a Build-to-Destination). The following article presents some of the aspects you should consider when choosing between them. Sisense recommends that you discuss your needs and requirements with Sisense's technical team during the Jumpstart process, so the result will best meet your business expectations. Table of Contents Definitions The ElastiCube Data Model Importing data into an ElastiCube data model allows the customer to pull data from multiple data sources on-demand or at a scheduled time, and create a single source of truth inside Sisense. The imported data can then be transformed and aggregated to meet your business needs. Once imported, the data snapshot is used to generate analytical information. The process of importing the data, known as a "Build", includes the following steps: Extract the data: Query the different data source(s) for data. Load the data: Write the data extracted to Sisense (the local MonetDB). Transform the data: Transform the local MonetDB (using SQL queries). To read more about ElastiCubes, see Introducing ElastiCubes. The Live Data Model Using a Live data model does not require importing data. Only the data's schema needs to be defined. Once configured, analytical information required by the user is queried directly against the backend data source. To read more about Live models, see Introducing Live Models. Determining Factors Refresh Rate One of the most fundamental aspects of determining your data model is your data's refresh rate. The data refresh rate refers to the age of the data in your dashboards: For Live models, the data displayed on your dashboards is near-real-time, as every query is passed directly to the backend database. A good example of using a live model (due to refresh rate requirements) is a dashboard that shows stock prices. For ElastiCubes, the data displayed on your dashboard is current to the last successful build event. Every query is passed to the local database for execution. A good example of using an ElastiCube (due to refresh rate requirements) is a dashboard that shows historical stock prices. In this case, a daily ETL process will provide results that are good enough. To make a choice based on this factor, answer the following questions: How frequently do I need to pull new data from the database? Do all my widgets require the same data refresh frequency? How long does an entire ETL process take? Data Transformation Options The ETL process includes a "Transformation" phase. This transformation phase usually includes: Migrating the data tables into a dim-fact schema Enriching your data Pre-aggregating the data to meet your business needs The amount of data transformation on Sisense helps determine the suitable data model: For Live models, Sisense allows minimal to no data transformation. Data is not imported before a query is issued from the front end. Therefore, data cannot be pre-conditioned or pre-aggregated. Most data sources used by Live models are data warehouses that may perform all data preparations themselves. For ElastiCubes, data is imported before a query is issued from the front end. Therefore, it may be pre-conditioned and pre-aggregated. A user may customize the data model to optimally answer their business questions. To make a choice based on this factor, answer the following questions: Is my data in a fact-dim schema? Does my data require enriching or pre-conditioning? Can my data be pre-aggregated? Operational Database Load Your operational databases do more than serve your analytical system. Any application loading the operational databases should be closely examined: For Live models, Sisense will constantly query information from your operational databases, and feed it into your dashboard widgets. This occurs every time a user loads a dashboard. For ElastiCubes, Sisense highly stresses your operational databases during an ETL process while reading all tables. To make a choice based on this factor, answer the following questions: Does the analytical system stress my operational database(s)? Can the query load be avoided by using a "database replica"? Operational Database Availability Your operational database(s) availability is critical for collecting information for your analytical system. For Live models, all queries are redirected to your data sources. If the data source is not available, widgets will generate errors and not present any data. For ElastiCubes, data source availability is critical during the ETL process. If the data source is not available, the data in your widgets will always be available, but not necessarily be up to date. To make a choice based on this factor, answer the following questions: How frequently are analytical data sources offline? How critical is my analytical system? Is being offline (showing out-of-date information) acceptable? Additional Vendor Costs Various database vendors use a chargeback charging model, meaning that you will be charged by the amount of data you pull from the database or the computational power required to process your data. For Live models, every time a user loads a dashboard, each widget will trigger (at least) one database query. A combination of a chargeback charging model and a large user load may result in high costs. For ElastiCubes, every time the user triggers an ETL process, a large amount of data is queried from the database and loaded into Sisense. To make a choice based on this factor, answer the following questions: What is the number of users using my dashboards / What is my "build" frequency? Which data model will result in lower costs? What is the tipping point? Are you willing to pay more for real-time data? Database Size For ElastiCubes, please refer to these documents: Introducing ElastiCubes Minimum Requirements for Sisense in Linux Environments For Live models, there is no limitation as data is not imported to Sisense, only the data's schema. To make a choice based on this factor, answer the following questions: What is the amount of data I need in my data model? What is the amount of history I need to store? Can I reduce the amount of data (e.g., trimming historical data? reducing the number of columns? etc.) Query Performance Query performance depends on the underlying work required to fetch data and process it. Although every widget generates a query, the underlying data model will determine the work necessary to execute it. For ElastiCubes, every query is handled inside Sisense: The client-side widget sends a JAQL query to the Sisense analytical system. The query Is translated into SQL syntax, and run against an internal database. The query result is transformed back to JAQL syntax and returned to the client-side. For Live models, every query is forwarded to an external database and then processed internally: The client-side widget sends a JAQL query to the Sisense analytical system. The query Is translated into SQL syntax, and run against an external database. Sisense waits for the query to execute. Once returned, the query result is transformed back into JAQL syntax and returned to the client-side. To make a choice based on this factor, answer the following questions: How sensitive is the client to a delay in the query's result? When showing real-time data, is this extra latency acceptable? Connector Availability Sisense supports hundreds of data connectors (see Data Connectors). However, not all connectors are available for live data models. The reasoning behind this has to do with the connector's performance. A "slow connector" or one that requires a significant amount of processing may lead to a bad user experience when using Live models (that is, widgets take a long time to load): For ElastiCubes, Sisense allows the user to utilize all the data connectors. For Live models, Sisense limits the number of data connectors to a few high-performing ones (including most data warehouses and high-performing databases). To make a choice based on this factor, answer the following questions: Does my data source's connector support both data model types? Should I consider moving my data to a different data source to allow live connectivity? Caching Optimization Sisense optimizes performance by caching query results. In other words, query results are stored in memory for easier retrieval, in case they are re-executed. This ability provides a great benefit and improves the end-user experience: For ElastiCubes, Sisense recycles (caches) query results. For Live models, Sisense performs minimal caching to make sure data is near real-time. (Note that caching can be turned off upon request.) To make a choice based on this factor, answer the following questions: Do I want to leverage Sienese's query caching? How long do I want to cache data? Dashboard Design Limitations Specific formulas (such as Mode and Standard Deviation) and widget types (such as Box plots or Whisker plots) may result in "heavy" database queries: For Live models, Sisense limits the use of these functions and visualizations as the results of these formulas and visualizations may take a long time, causing a bad user experience. For ElastiCubes, Sisense allows the user to use them, as processing them is internal to Sisense. To make a choice based on this factor, answer the following questions: Do I need these functions and visualizations? Can I pre-aggregate the data and move these calculations to the data source instead of Sisense? See also Choosing a Data Strategy for Embedded Self-Service.The Ability To Manage JDBC Connections
Question How to change the connection strings in an easy way? Answer Using the REST API you can update the data connection strings directly. Returns a list of all your connections: GET http://localhost:8081/api/v1/connection Updates a connection: PATCH http://localhost:8081/api/v1/connection/{id}Personio API Pre-Processing Data
Question Is there a way to add pre-processing (python script) to the data that will be uploaded from a custom API connector? Or should I run the script locally on a fixed schedule and use the Sisense API to upload after the data is processed? Answer Some info about your questions (as we already come across with that many times before): You can run any python script that you would like automatically before or after a Build is done using an ElastiCube Plugin , scroll to the part where is talks about python scripts. Sisense doesn't provide an API to modify data within your cube. So if you would like the Python script to do some extra processing before it gets pushed to the cube - you can save that data locally on the Sisense machine in csv format and then use another another post-build plugin to delete that csv once the build is finished. - Notice that you can use any python version you'd like (in their code sample they are using the old 2.7)
Reducing Windows Memory Pressure by Removing unused JVM Connectors
Reducing Windows Memory Pressure by Removing unused JVM Connectors A simple technique can reduce memory pressure on Windows Sisense versions by inactivating unused JM connectors. To do this you should search for the Sisense JVM Connectors Configuration application from the Windows Start menu. After opening the Sisense JVM Connectors Configuration application simply uncheck any connectors which are not in use and press the Save button. The amount of memory saved varies depending on which connector. On average each connector uses 2GB. So if you inactivate 5 connectors, you just saved 10 GB of memory. If you need additional help, please contact Sisense Support. PLEASE NOTE: If you cannot locate the JVM as seen in the screenshot, the executable name is "usedConnectorsEditor.bat" and it should be located in the following directory C:\Program Files\Sisense\DataConnectors\JVMContainer\bin As seen in below picture.Google Analytics CDATA Connector
In May 2022, after Google’s April announcement deprecating an old API, Sisense announced that we will be deprecating the native Google Analytics connector. Despite these hurdles, users can still use CDATA drivers as a workaround in order to connect. This article will show you two ways of using a CDATA driver to connect to a Google Analytics data source.
