案例需求全部满足 ➢ 生产环境实验同步 ➢ 支持全量同步、增量实时同步，无延时 ➢ 支持多种数据同构、异构数据同步 ➢ 过滤器制作规则处理异常数据 ➢ 可以达到BI系统的实时要求 ➢ 网络带宽利用率低，且支持数据的安全传输 一些事前没有考虑到的问题： 1. 数据质量问题 0000-00-00 00:00:00 28:00:00 2. 数据乱码问题 3. DDL复制 4. 生产端历史数据purge问题

语言算法库和程序包。 此外，Greenplum 还支持最新版 Apache MADlib（可用 SQL 进行机器学习、深度学习和图分析），它支持高并行 和基于 GPU 的深度学习模型训练，内置于集群硬件中的 GPU，能帮助 Greenplum 6 的用户获得超过 CPU 2 个数量 级的性能加速，尤其对于可预测的分析用例和图像识别，这些功能将展现奇效。支持在 Apache Solr 数据库内实施

配置基于内存限制的查询终止 当有全局共享内存时，runaway_detector_activation_percent 参数设置 了全局共享内存的阈值，当全局共享内存达到参数指定的利用率时，将触发资源组中的 查询被终止，这只针对内存管理模式为 vmtracker 的资源组。编者的理解是，当阈 值达到时，那些继续向全局共享内存申请配额的事务将会被终止，其他不需要向全局共 享内存申请配额的事务将不受影响。 对大表进行数据重分布的时间较长，所以，占有访问排它锁的时间也会较长，因此， 应该安排在业务空闲时段对大表进行数据重分布。所以，可以在业务空闲时段，使用大 并发(gpexpand -n参数指定并发度)来提高系统资源的利用率，以加速数据重分布的 进度，同时，应该控制数据库为业务提供服务的连接数或者并发事务的数量，为数据重 分布操作保留必要的资源。 Greenplum Database 管理员指南 V6.2 状态，并能 够及时采取措施进行修复。  定期监控磁盘使用率，在必要时，提前规划扩容。GP数据库要求，磁盘使用率不 能超过70%，这是一条永远都不应该触碰的红线，GP数据库长期运行在磁盘利用率 高于70%的状态，可能会导致多种难以控制的问题，比如，磁盘碎片率突增，磁盘 性能陡降，最终体现在数据库上的是，性能下降严重，数据库效率低下。  定期检查主机之间的磁盘容量是否发生倾斜。

Elastic Net • Improved Python Language Support • Investigation on Graph Support • Investigation on GPU support • Performance improvements 2016Postgres中国用户大会 Postgres Conference China 2016 中国用户大会 GPDB

was updated to add the regex package. The package versions for lxml, nltk, tenserflow, tenserflow-gpu, and keras were updated to address CVEs. pg_dump performance was improved to reduce the time required update Microsoft Word .docx files PyTorch Tensors and Dynamic neural networks in Python with strong GPU acceleration pytz World timezone definitions, modern and historical regex Alternative regular expression inter-process locks zipp Backport of pathlib-compatible object wrapper for zip files tensorflow-gpu An open source software library for high performance numerical computation tensorflow Numerical computation

was updated to add the regex package. The package versions for lxml, nltk, tenserflow, tenserflow-gpu, and keras were updated to address CVEs. pg_dump performance was improved to reduce the time required update Microsoft Word .docx files PyTorch Tensors and Dynamic neural networks in Python with strong GPU acceleration pytz World timezone definitions, modern and historical regex Alternative regular expression inter-process locks zipp Backport of pathlib-compatible object wrapper for zip files tensorflow-gpu An open source software library for high performance numerical computation tensorflow Numerical computation

Greenplum 6 Documentation VMware, Inc 140 package versions for lxml, nltk, tenserflow, tenserflow-gpu, and keras were updated to address CVEs. pg_dump performance was improved to reduce the time required update Microsoft Word .docx files PyTorch Tensors and Dynamic neural networks in Python with strong GPU acceleration pytz World timezone definitions, modern and historical regex Alternative regular expression inter-process locks zipp Backport of pathlib-compatible object wrapper for zip files tensorflow-gpu An open source software library for high performance numerical computation tensorflow Numerical computation

was updated to add the regex package. The package versions for lxml, nltk, tenserflow, tenserflow-gpu, and keras were updated to address CVEs. pg_dump performance was improved to reduce the time required Module Name Description/Used For PyTorch Tensors and Dynamic neural networks in Python with strong GPU acceleration pytz World timezone definitions, modern and historical regex Alternative regular expression inter-process locks zipp Backport of pathlib-compatible object wrapper for zip files tensorflow-gpu An open source software library for high performance numerical computation tensorflow Numerical computation

update Microsoft Word .docx files PyTorch Tensors and Dynamic neural networks in Python with strong GPU acceleration pytz World timezone definitions, modern and historical regex Alternative regular expression algorithms using popular frameworks like Keras and TensorFlow in an MPP relational database, with GPU (Graphical Processing Unit) acceleration. Use containers capable of isolating executors from the host training of deep neural networks so they are typically used for enterprise level workloads. For further GPU information, visit the MADlib wiki, https://cwiki.apache.org/confluence/display/MADLIB/Deep+Learning

was updated to add the regex package. The package versions for lxml, nltk, tenserflow, tenserflow-gpu, and keras were updated to address CVEs. pg_dump performance was improved to reduce the time required update Microsoft Word .docx files PyTorch Tensors and Dynamic neural networks in Python with strong GPU acceleration pytz World timezone definitions, modern and historical regex Alternative regular expression inter-process locks zipp Backport of pathlib-compatible object wrapper for zip files tensorflow-gpu An open source software library for high performance numerical computation tensorflow Numerical computation