Please share the complete error stack-trace.  With respect to The table doesn't have partitions. Make sure HDFS and metadata in sync.  ... View more

Grafana is a popular open-source platform for monitoring and observability, and it is commonly associated with telemetry data visualization, especially when integrated with time-series databases like Prometheus, InfluxDB, or Elasticsearch. However, Grafana is not limited to telemetry data visualization, and it can be used for a wide range of data sources, including HDFS and Hive tables. Here are some options for using Grafana for data visualization beyond telemetry: Hive Data Sources: Grafana has built-in support for various data sources, and it offers plugins for connecting to databases and data lakes. You can configure Grafana to connect to Hive as a data source and visualize data stored in Hive tables. HDFS Data Sources: While Grafana primarily focuses on time-series data, you can still use it to visualize data stored in HDFS by connecting it to Hadoop-related data sources or by exporting HDFS data to another data store (e.g., Elasticsearch, InfluxDB) that Grafana supports. SQL Databases: Grafana can connect to traditional relational databases using SQL data sources. If you have data stored in SQL databases, you can use Grafana to create dashboards and visualizations. Log Data: Grafana can be used for log data analysis and visualization. You can integrate it with tools like Loki (for log aggregation) and explore log data in dashboards. Custom Plugins: If you have a unique data source or a specific format, you can develop custom data source plugins for Grafana to connect to your data and visualize it as needed. API Data: Grafana supports various data sources that expose data through APIs. You can connect to REST APIs, GraphQL APIs, and other web services to visualize data. Mixed Data Sources: Grafana allows you to create dashboards that combine data from multiple sources, making it versatile for various data visualization needs. While Grafana is flexible and can be used for a wide range of data sources, it's important to consider the nature of your data and the specific visualization requirements. Depending on your use case, you may need to choose the most suitable data source, data format, and visualization options within Grafana to achieve your desired results. ... View more

When users query a Hive table partitioned on a specific column (in your case, "source system name") but do not include a filter condition on that partition column in their queries, Hive may need to scan all partitions of the table to retrieve the relevant data. This can lead to less efficient query performance, as it requires reading unnecessary data from multiple partitions. In your scenario, where you perform frequent insert overwrites to keep only the current data, the table may not grow drastically in terms of total data volume. However, if the users frequently query the table without specifying the partition column condition, it can still result in increased query processing time and resource utilisation. To improve query efficiency in this situation, you have a few options: Partition Pruning: Encourage users to include the partition column condition in their queries. Hive has built-in partition pruning optimization, which allows it to skip unnecessary partitions when the partition column condition is provided. Materialized Views: If certain common query patterns exist, consider creating materialized views that pre-aggregate or pre-filter data based on those patterns. This can significantly speed up queries that align with the materialized views. Optimize Data Layout: Ensure that the data is stored efficiently, and consider using columnar storage formats like ORC or Parquet, which can improve query performance. Ultimately, the choice of optimization strategy depends on the specific usage patterns and requirements of your users. It's essential to monitor query performance and understand your users' query behavior to determine which optimization approaches are most effective. ... View more

Could you kindly provide the DDL and a sample dataset to facilitate a more in-depth explanation? ... View more

It appears that you're currently following a two-step process: writing data to a Parquet table and then using that Parquet table to write to an ORC table. You can streamline this by directly writing the data into the ORC format table, eliminating the need to write the same data to a Parquet table before reading it.  Ref -  https://spark.apache.org/docs/2.4.0/sql-data-sources-hive-tables.html https://docs.cloudera.com/cdp-private-cloud-base/7.1.9/developing-spark-applications/topics/spark-loading-orc-data-predicate-push-down.html   ... View more

To pinpoint the root cause, kindly provide a few samples of data ... View more

It seems that the failure occurred within the Tez job's child tasks. To identify the root cause, please share the YARN logs of the failed application ... View more