performance tradeoff. Next, the chapter goes over weight sharing using clustering. Weight sharing, and in particular clustering is a generalization of quantization. If you noticed, quantization ensures range. It creates equal sized quantization ranges (bins), regardless of the frequency of data. Clustering helps solve that problem by adapting the allocation of precision to match the distribution of the a convolutional layer which receives a 3-channel input. Each individual 3x3 matrix is a kernel. A column of 3 kernels represents a channel. As you might notice, with such structured sparsity we can obtain

See the [Transport and DOSGi] section of the user guide. Finally, Cellar also provides "runtime clustering" by providing dedicated feature like: • HTTP load balancing • HTTP sessions replication • log |grep -i cellar cellar-core | 3.0.3 | | karaf-cellar-3.0.3 | Karaf clustering core hazelcast | 3.4.2 | | karaf-cellar-3.0.3 | In memory data shell support cellar | 3.0.3 | | karaf-cellar-3.0.3 | Karaf clustering cellar-dosgi | 3.0.3 | | karaf-cellar-3.0.3 | DOSGi support cellar-obr

See the transport and DOSGi section of the user guide. Finally, Cellar also provides "runtime clustering" by providing dedicated feature like: * HTTP load balancing * HTTP sessions replication * log you can note there the "Located" column containing where the feature is located (local to the node, or on the cluster group). You can also see the "Blocked" column indicating if the feature is blocked add/remove any PID he wishes to the whitelist/blacklist. 3. The role of Hazelcast The idea behind the clustering engine is that for each unit that we want to replicate, we create an event, broadcast the event

Performance CHAPTER FIVE AUTHENTICATION & AUTHORIZATION With strong authentication and fine-grained column/row level authorization, Kyuubi keeps your system and data secure. 11 Kyuubi, Release 1.5.0-incubating --showHeader=[true/false] show column names in query results --headerInterval=ROWS; the interval between which heades are displayed --fastConnect=[true/false] skip building table/column list for tab-completion optimal display column sizing. When set to true, result rows␣ ˓→are displayed immediately as they are fetched, yielding lower␣ ˓→latency and memory usage at the price of extra display column␣ ˓→padding

Performance CHAPTER FIVE AUTHENTICATION & AUTHORIZATION With strong authentication and fine-grained column/row level authorization, Kyuubi keeps your system and data secure. 11 Kyuubi, Release 1.5.1-incubating --showHeader=[true/false] show column names in query results --headerInterval=ROWS; the interval between which heades are displayed --fastConnect=[true/false] skip building table/column list for tab-completion optimal display column sizing. When set to true, result rows␣ ˓→are displayed immediately as they are fetched, yielding lower␣ ˓→latency and memory usage at the price of extra display column␣ ˓→padding

Performance CHAPTER FIVE AUTHENTICATION & AUTHORIZATION With strong authentication and fine-grained column/row level authorization, Kyuubi keeps your system and data secure. 11 Kyuubi, Release 1.5.2-incubating --showHeader=[true/false] show column names in query results --headerInterval=ROWS; the interval between which heades are displayed --fastConnect=[true/false] skip building table/column list for tab-completion optimal display column sizing. When set to true, result rows␣ ˓→are displayed immediately as they are fetched, yielding lower␣ ˓→latency and memory usage at the price of extra display column␣ ˓→padding

provides engine authorization extensions to optimize the data security model to fine-grained row/column level. Please see the security page for more information. 1.3 High Availability Kyuubi is designed enabled by default, which only provides file-level coarse-grained au- thorization mode. When row/column-level fine-grained access control is required, we can enhance the data access model with the Kyuubi enables Kyuubi with data and metadata control access ability for Spark SQL Engines, including, • Column-level fine-grained authorization • Row-level fine-grained authorization, a.k.a. Row-level filtering

provides engine authorization extensions to optimize the data security model to fine-grained row/column level. Please see the security page for more information. 1.3 High Availability Kyuubi is designed enabled by default, which only provides file-level coarse-grained au- thorization mode. When row/column-level fine-grained access control is required, we can enhance the data access model with the Kyuubi enables Kyuubi with data and metadata control access ability for Spark SQL Engines, including, • Column-level fine-grained authorization • Row-level fine-grained authorization, a.k.a. Row-level filtering

provides engine authorization extensions to optimize the data security model to fine-grained row/column level. Please see the security page for more information. 1.3 High Availability Kyuubi is designed enabled by default, which only provides file-level coarse-grained au- thorization mode. When row/column-level fine-grained access control is required, we can enhance the data access model with the Kyuubi enables Kyuubi with data and metadata control access ability for Spark SQL Engines, including, • Column-level fine-grained authorization • Row-level fine-grained authorization, a.k.a. Row-level filtering

provides engine authorization extensions to optimize the data security model to fine-grained row/column level. Please see the security page for more information. 1.3 High Availability Kyuubi is designed enabled by default, which only provides file-level coarse-grained au- thorization mode. When row/column-level fine-grained access control is required, we can enhance the data access model with the Kyuubi enables Kyuubi with data and metadata control access ability for Spark SQL Engines, including, • Column-level fine-grained authorization • Row-level fine-grained authorization, a.k.a. Row-level filtering