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10-10-2023
10:15 AM
Please share the complete error stack-trace. With respect to The table doesn't have partitions. Make sure HDFS and metadata in sync.
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10-10-2023
10:06 AM
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.
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10-10-2023
09:58 AM
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.
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10-09-2023
06:27 AM
Could you kindly provide the DDL and a sample dataset to facilitate a more in-depth explanation?
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10-04-2023
02:33 AM
1 Kudo
In Hive, there is no specific built-in data type that directly corresponds to the SQL Image data type for retaining the original binary image data from an SQL source. Hive primarily deals with structured data types like strings, numbers, and complex types such as arrays, maps, and structs. To store binary data like images in Hive, you typically use the BINARY data type or store them as STRING data, especially if you want to represent them in base64-encoded format. However, neither of these data types inherently retains the original binary value as is. You would need to handle the encoding and decoding of the binary data yourself. Here's an example of how you might store binary image data in Hive using the BINARY data type: CREATE TABLE image_data (
image_id INT,
image_content BINARY
); When you insert data into this table, you would need to encode the binary image data into a binary format suitable for storage in Hive. If retaining the original binary image data in its original format is crucial, you may want to consider other data storage solutions that are specifically designed for binary data, such as a distributed file system or binary data storage services. Hive, being primarily designed for structured data, may not be the best choice for this use case if you need to maintain the exact original binary data without encoding or modification.
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10-03-2023
02:15 AM
It seems that the query involves dynamic partitioning, but the dynamic partition column is not included in either the select statement or the Common Table Expression (CTE). Please add the dynamic partition column 'date' to the select statement and validate it in Beeline.
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10-03-2023
02:07 AM
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
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10-03-2023
01:58 AM
You can use the following HQL (SQL) query to find the sum of new cases for each continent and identify the country with the highest number of cases in each continent along with the corresponding case count: WITH ContinentSum AS (
SELECT
continent,
SUM(new_cases) AS total_new_cases
FROM
sample_table_test
GROUP BY
continent
),
CountryMaxCases AS (
SELECT
continent,
location AS country,
MAX(total_case) AS max_cases
FROM
sample_table_test
GROUP BY
continent, location
)
SELECT
cs.continent,
cs.total_new_cases,
cm.country,
cm.max_cases
FROM
ContinentSum cs
JOIN
CountryMaxCases cm
ON
cs.continent = cm.continent
AND cs.total_new_cases = cm.max_cases; This query first calculates the sum of new cases for each continent in the ContinentSum CTE (Common Table Expression). Then, it finds the country with the highest total cases in each continent using the CountryMaxCases CTE. Finally, it joins the results from both CTEs to provide the desired output. Sample resultset for the shared data. +---------------+---------------------+--------------+---------------+
| cs.continent | cs.total_new_cases | cm.country | cm.max_cases |
+---------------+---------------------+--------------+---------------+
| Asia | 25.0 | Afghanistan | 25.0 |
+---------------+---------------------+--------------+---------------+
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09-30-2023
11:39 AM
To pinpoint the root cause, kindly provide a few samples of data
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09-30-2023
11:36 AM
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
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