See how extreme query speeds and ultra-high concurrency on MicroStrategy, and any other business intelligence (BI) tool, on Big Data is possible through the Kyligence platform. Learn more here: https://kyligence.io/
Azure storage to be generic, replace Alibaba with Hadoop
灵活的多维建模
模型的变化只影响有关的索引;
模型定义的变化与数据加载互不影响;
--------------------
Flexible multidimensional modeling
Changes in the model affect only the relevant indexes
Changes in model definitions and data loading do not affect each other
灵活的多维建模
模型的变化只影响有关的索引;
模型定义的变化与数据加载互不影响;
--------------------
Flexible multidimensional modeling
Changes in the model affect only the relevant indexes
Changes in model definitions and data loading do not affect each other
Industry-recognized data analysis test data sets Analysis of key business decisions Practical business significance
0.5 billion dataset, test TPC-H 22 queries.
Test method: 3 times to average, no query engine to warm up.
TPC-H Benchmark
Examine large volumes of data
High complexity queries
Answers critical business questions
22 decision making queries
E.g. The Shipping Priority Query
retrieves the shipping priority and potential revenue of the orders having the largest revenue among those that had not been shipped as of a given date. Top 10 orders are listed in decreasing order of revenue.
HARDWARE CONFIGURATION
Same 4 physical nodes
Intel(R) Xeon(R) CPU E5-2630 v4 @ 2.20GHz * 2
Totally 86 vCores, 188 GB mem
Same Spark configuration for both KE 4 Beta and SparkSQL 2.4
spark.driver.memory=16g
spark.executor.memory=8g
spark.yarn.executor.memoryOverhead=2g
spark.yarn.am.memory=1024m
spark.executor.cores=5
spark.executor.instances=17
Query Response Time | 5 Billion
Same 4 physical nodes
Intel(R) Xeon(R) CPU E5-2630 v4 @ 2.20GHz * 2
Totally 86 vCores, 188 GB mem
Same Spark configuration for both KE 4 Beta and SparkSQL 2.4
spark.driver.memory=16g
spark.executor.memory=20g
spark.yarn.executor.memoryOverhead=2g
spark.yarn.am.memory=1024m
spark.executor.cores=5
spark.executor.instances=30
Benefits:
Unlimited scale-out solution to fit future data volume growth
1 hour non-blocking incremental loading
Single cube easy maintenance
Low infrastructure cost with auto scaling
support 100 concurrent users
Transparent to business users, same
analysis tool Excel
Architecture
Kyligence Enterprise 4.0
Azure HDInsight 3.6
Azure Data Lake gen2
Cluster size: 30 D3 V2 worker nodes
(potentially) ingest data from Oracle
Query performance
90% SQL queries within 5s
90% MDX queries within 60s
80% MDX queries within 20s
50 QPS per query node