Create async-event-queues, disk-stores, gateway receivers, gateway senders, indexes, and regions.
-
Creates an asynchronous event queue for batching events before they are delivered by a gateway sender.
-
Creates all the defined indexes.
-
Defines a pool of one or more disk stores, which can be used by regions and client subscription queues, and gateway sender queues for WAN distribution.
-
Creates a gateway receiver. You can only have one gateway receiver on each member, and unlike a gateway sender, you do not need to specify an identifier for the gateway receiver .
-
Creates a gateway sender on one or more members of a distributed system.
-
Create an index that can be used when executing queries.
-
Create a JNDI binding that specifies resource attributes which describe a JDBC connection.
-
Create a region with given path and configuration.
-
Create a region with given path and configuration.
create async-event-queue
Creates an asynchronous event queue for batching events before they are delivered by a gateway sender.
See Configuring Multi-Site (WAN) Event Queues.
Availability: Online. You must be connected in gfsh
to a JMX Manager member to use this command.
Syntax:
create async-event-queue --id=value --listener=value [--groups=value(,value)*]
[--parallel(=value)?] [--enable-batch-conflation(=value)?] [--batch-size=value]
[--batch-time-interval=value] [--persistent(=value)?] [--disk-store=value]
[--disk-synchronous(=value)?] [--max-queue-memory=value]
[--dispatcher-threads=value] [--order-policy=value]
[--gateway-event-filter=value(,value)*]
[--gateway-event-substitution-filter=value]
[--listener-param=value(,value)*]
Parameters, create async-event-queue:
| Name | Description | Default Value |
|---|---|---|
| –id | Required. ID of the asynchronous event queue | |
| –groups | The queue is created on all members of the group(s). If you do not specify a group, the queue is created on all members. | |
| –parallel | Specifies whether the queue is parallel. | false |
| –enable-batch-conflation | Enables batch conflation. | false |
| –batch-size | Maximum number of messages that a batch can contain. | 100 |
| –batch-time-interval | Maximum amount of time, in ms, that can elapse before a batch is delivered. | 5 |
| –persistent | Boolean value that determines whether Geode persists this queue. | false
If specified with out a value, default is true. |
| –disk-store | Named disk store to use for storing queue overflow, or for persisting the queue. If you specify a value, the named disk store must exist. If you specify a null value, Geode uses the default disk store for overflow and queue persistence. | |
| –disk-synchronous | Specifies whether disk writes are synchronous. | true |
| –max-queue-memory | Maximum amount of memory in megabytes that the queue can consume before overflowing to disk. | 100 |
| –dispatcher-threads | Number of threads used for sending events. | 5 |
| –order-policy | Policy for dispatching events when –dispatcher-threads is > 1. Possible values are THREAD , KEY , PARTITION . |
KEY |
| –gateway-event-filter | List of fully qualified class names of GatewayEventFilters for this queue. These classes filter events before dispatching to remote servers. | |
| –gateway-event-substitution-filter | Fully-qualified class name of the GatewayEventSubstitutionFilter for this queue. |
|
| –listener | Required. Fully-qualified class name of Async Event Listener for this queue | |
| –listener-param | Parameter name and value to be passed to the Async Event Listener class. Optionally, you can specify a value by following the parameter name with the # character and the value. For example:
|
|
| –forward-expiration-destroy | Enables forwarding of expiration destroy operations to AsyncEventListener instances. If specified without a value, this parameter is set to “false”. | false |
Example Commands:
create async-event-queue --id=myAEQ --listener=myApp.myListener
create defined indexes
Creates all the defined indexes.
See also define index and clear defined indexes.
Availability: Online. You must be connected in gfsh to a JMX Manager member to use this command.
Syntax:
create defined indexes [--members=value(,value)*] [--groups=value(,value)*]
Parameters, create defined indexes:
| Name | Description | Default |
|---|---|---|
| --members | Name/Id of the member(s) on which index will be created. | |
| --groups | The index will be created on all the members in the member group(s). |
Example Commands:
create defined indexes
Sample Output:
gfsh>create defined indexes
Indexes successfully created. Use list indexes to get details.
1. ubuntu(server1:17682)<v1>:27574
If index creation fails, you may receive an error message in gfsh similar to the following:
gfsh>create defined indexes
Exception : org.apache.geode.cache.query.RegionNotFoundException ,
Message : Region ' /r3' not found: from /r3Occurred on following members
1. india(s1:17866)<v1>:27809
create disk-store
Defines a pool of one or more disk stores, which can be used by regions and client subscription queues, and gateway sender queues for WAN distribution.
See Disk Storage
Availability: Online. You must be connected in gfsh to a JMX Manager member to use this command.
Syntax:
create disk-store --name=value --dir=value(,value)* [--allow-force-compaction(=value)?]
[--auto-compact(=value)?] [--compaction-threshold=value] [--max-oplog-size=value]
[--queue-size=value] [--time-interval=value] [--write-buffer-size=value]
[--groups=value(,value)*]
[--disk-usage-warning-percentage=value] [--disk-usage-critical-percentage=value]
Parameters, create disk-store:
| Name | Description | Default Value |
|---|---|---|
| --name | Required. The name of this disk store. | |
| --dir | Required. One or more directory names where the disk store files are written. Optionally, directory names may be followed by # and the maximum number of megabytes that the disk store can use in the directory. For example:
If the specified directory does not exist, the command will create the directory for you. |
|
| --allow-force-compaction | Set to true to allow disk compaction to be forced on this disk store. | false |
| --auto-compact | Set to true to automatically compact the disk files. | true |
| --compaction-threshold | Percentage of garbage allowed before the disk store is eligible for compaction. | 50 |
| --max-oplog-size | Maximum size, in megabytes, for an oplog file. When the oplog file reaches this size, the file is rolled over to a new file. | 1024 |
| --queue-size | Maximum number of operations that can be asynchronously queued to be written to disk. | 0 |
| --time-interval | The number of milliseconds that can elapse before unwritten data is written to disk. | 1000 |
| –groups | The disk store is created on all members of the group(s). If no group is specified, the disk store is created on all members. | |
| --write-buffer-size | The size of the write buffer that this disk store uses when writing data to disk. Larger values may increase performance but use more memory. The disk store allocates one direct memory buffer of this size. | 32768 |
| --disk-usage-warning-percentage | Disk usage above this threshold generates a warning message. For example, if the threshold is set to 90%, then on a 1 TB drive falling under 100 GB of free disk space generates the warning.
Set to “0” (zero) to disable. |
90 |
| --disk-usage-critical-percentage | Disk usage above this threshold generates an error message and shuts down the member’s cache. For example, if the threshold is set to 99%, then falling under 10 GB of free disk space on a 1 TB drive generates the error and shuts down the cache.
Set to “0” (zero) to disable. |
99 |
Example Commands:
create disk-store --name-store1 --dir=/data/ds1
Sample Output:
gfsh>create disk-store --name-store1 --dir=/data/ds1
Member | Result
------- | -------
server1 | Success
create gateway-receiver
Creates a gateway receiver. You can only have one gateway receiver on each member, and unlike a gateway sender, you do not need to specify an identifier for the gateway receiver.
See Gateway Receivers.
Availability: Online. You must be connected in gfsh
to a JMX Manager member to use this command.
Syntax:
create gateway-receiver [--groups=value(,value)*] [--members=value(,value)*]
[--manual-start=value] [--start-port=value] [--end-port=value] [--bind-address=value]
[--maximum-time-between-pings=value] [--socket-buffer-size=value]
[--gateway-transport-filter=value(,value)*] [--hostname-for-senders=value]
Parameters, create gateway-receiver:
| Name | Description | Default Value |
|---|---|---|
| --groups | Gateway receivers are created on the members of the group(s). | |
| --members | Name of the distributed system member(s) on which to create the gateway receiver. | |
| --manual-start | Boolean value that specifies whether you need to manually start the gateway receiver. If you supply a null value, the default is “false” the gateway receiver starts automatically. | true |
| --start-port | Starting port number to use when specifying the range of possible port numbers this gateway receiver will use to connects to gateway senders in other sites. Geode chooses an unused port number in the specified port number range to start the receiver. If no port numbers in the range are available, an exception is thrown. The |
5000 |
| --end-port | Defines the upper bound port number to use when specifying the range of possible port numbers this gateway receiver will use to for connections from gateway senders in other sites. Geode chooses an unused port number in the specified port number range to start the receiver. If no port numbers in the range are available, an exception is thrown. The |
5500 |
| --bind-address | Network address for connections from gateway senders in other sites. Specify the address as a literal string value. | |
| --socket-buffer-size | An integer value that sets the buffer size (in bytes) of the socket connection for this gateway receiver. This value should match the socket-buffer-size setting of gateway senders that connect to this receiver. |
32768 |
| --gateway-transport-filter | The fully qualified class name of the GatewayTransportFilter to be added to the Gateway receiver. | |
| --maximum-time-between-pings | Integer value that specifies the time interval (in milliseconds) to use between pings to connected WAN sites. This value determines the maximum amount of time that can elapse before a remote WAN site is considered offline. | 60000 |
| --hostname-for-senders | The host name or IP address told to gateway senders as the address for them to connect to. The locator informs gateway senders of this value. |
Example Commands:
gfsh>create gateway-receiver --members=server1
Sample Output:
gfsh>create gateway-receiver --members=server1
Member | Status
------- | ---------------------------------------------------------------------------
server1 | GatewayReceiver created on member "server1" and will listen on the port "0"
create gateway-sender
Creates a gateway sender on one or more members of a distributed system.
See Gateway Senders.
Note:
The gateway sender configuration for a specific sender id
must be identical on each Geode member that hosts the gateway sender.
Availability: Online. You must be connected in gfsh
to a JMX Manager member to use this command.
Syntax:
create gateway-sender --id=value --remote-distributed-system-id=value
[--groups=value(,value)*] [--members=value(,value)*] [--parallel=value]
[--manual-start=value] [--socket-buffer-size=value] [--socket-read-timeout=value]
[--enable-batch-conflation=value] [--batch-size=value] [--batch-time-interval=value]
[--enable-persistence=value] [--disk-store-name=value] [--disk-synchronous=value]
[--maximum-queue-memory=value] [--alert-threshold=value] [--dispatcher-threads=value]
[--order-policy=value][--gateway-event-filter=value(,value)*]
[--gateway-transport-filter=value(,value)*]
Parameters, create gateway-sender:
| Name | Description | Default |
|---|---|---|
| --id | Required. Unique identifier for the gateway sender, usually an identifier associated with a physical location. | |
| --remote-distributed-system-id | Required. ID of the remote distributed system where this gateway sender sends events. | |
| --groups | Gateway senders are created on the members of the group(s). | |
| --members | Name of the distributed system member(s) on which to create the gateway sender. | |
| --parallel | When set to true, specifies a parallel Gateway Sender. | false |
| --enable-batch-conflation | Boolean value that determines whether Geode should conflate messages. | false |
| --manual-start | Deprecated. Boolean value that specifies whether you need to manually start the gateway sender. If you supply a null value, the default value of false is used, and the gateway sender starts automatically. A manual start is likely to cause data loss, so manual start should never be used in a production system. | false |
| --socket-buffer-size | Size of the socket buffer that sends messages to remote sites. This size should match the size of the socket-buffer-size attribute of remote gateway receivers that process region events. |
32768 |
| --socket-read-timeout | Amount of time in milliseconds that the gateway sender will wait to receive an acknowledgment from a remote site. By default this is set to 0, which means there is no timeout. If you do set this timeout, you must set it to a minimum of 30000 (milliseconds). Setting it to a lower number will generate an error message and reset the value to the default of 0. | 0 |
| --batch-size | Maximum number of messages that a batch can contain. | 100 |
| --batch-time-interval | Maximum number of milliseconds that can elapse between sending batches. | 1000 |
| --enable-persistence | Boolean value that determines whether Geode persists the gateway queue. | false |
| --disk-store-name | Named disk store to use for storing the queue overflow, or for persisting the queue. If you specify a value, the named disk store must exist. If you specify a null value, Geode uses the default disk store for overflow and queue persistence. | |
| --disk-synchronous | For regions that write to disk, boolean that specifies whether disk writes are done synchronously for the region. | true |
| --maximum-queue-memory | Maximum amount of memory in megabytes that the queue can consume before overflowing to disk. | 100 MB |
| --alert-threshold | Maximum number of milliseconds that a region event can remain in the gateway sender queue before Geode logs an alert. | 0 |
| --dispatcher-threads | Number of dispatcher threads that are used to process region events from a gateway sender queue or asynchronous event queue. | 5 |
| --order-policy | When the dispatcher-threads attribute is greater than 1, order-policy configures the way in which multiple dispatcher threads process region events from a serial gateway queue or serial asynchronous event queue. This attribute can have one of the following values:
You cannot configure the |
key |
| --gateway-event-filter | A list of fully-qualified class names of GatewayEventFilters (separated by comma) to be associated with the GatewaySender. This serves as a callback for users to filter out events before dispatching to remote distributed system. For example:
|
|
| --gateway-transport-filter | The fully-qualified class name of the GatewayTransportFilter to be added to the GatewaySender. |
Example Commands:
gfsh>create gateway-sender --remote-distributed-system-id="2" --id="sender2"
Sample Output:
gfsh>create gateway-sender --remote-distributed-system-id="2" --id="sender2"
Member | Status
------- | --------------------------------------------
server1 | GatewaySender "sender2" created on "server1"
create index
Create an index that can be used when executing queries.
Availability: Online. You must be connected in gfsh
to a JMX Manager member to use this command.
See Working with Indexes.
Syntax:
create index --name=value --expression=value --region=value
[--members=value(,value)*] [--type=value] [--groups=value(,value)*]
Parameters, create index:
| Name | Description | Default |
|---|---|---|
| --name | Required. Name of the index to create. | |
| ‑‑expression | Required. Field of the region values that are referenced by the index. | |
| --region | Required. Name/Path of the region which corresponds to the “from” clause in a query. | |
| --members | Name/Id of the member(s) on which index will be created. | |
| --type | Type of the index. Valid values are: range and key . (A third type, hash , is still recognized but hash indexes are deprecated.) |
range |
| --groups | The index will be created on all the members in the group(s). |
Example Commands:
create index --name=myKeyIndex --expression=region1.Id --region=region1 --type=key
Sample Output:
gfsh>create index --name=myKeyIdex --expression=region1.Id --region=region1 --type=key
Index successfully created with following details
Name : myKeyIdex
Expression : region1.Id
RegionPath : /region1
Members which contain the index
1. ubuntu(server1:17682)<v1>:27574
gfsh>create index --name=myIndex2 --expression=exp2 --region=/exampleRegion
Failed to create index "myIndex2" due to following reasons
Index "myIndex2" already exists. Create failed due to duplicate name.
Occurred on following members
1. ubuntu(server1:17682)<v1>:27574
create jndi-binding
Create a JNDI binding that specifies resource attributes which describe a JDBC connection.
Availability: Online. You must be connected in gfsh to a JMX Manager member to use this command.
Syntax:
create jndi-binding --name=value --type=value --jdbc-driver-class=value
--connection-url=value [--blocking-timeout-seconds=value]
[--conn-pooled-datasource-class=value] [--idle-timeout-seconds=value]
[--init-pool-size=value] [--login-timeout-seconds=value]
[--managed-conn-factory-class=value] [--max-pool-size=value] [--password=value]
[--transaction-type=value] [--username=value] [--xa-datasource-class=value]
[--if-not-exists(=value)?] [--datasource-config-properties=value(,value)*]
Parameters, create jndi-binding:
| Name | Description | Default |
|---|---|---|
| --name | Required. Name of the binding to create. | |
| --type | Required. Type of the XA datasource. One of: MANAGED , SIMPLE , POOLED , or XAPOOLED . |
|
| --jdbc-driver-class | Required. The fully qualified name of the JDBC driver class. | |
| --connection-url | Required. the JDBC driver connection URL string. For example, jdbc:hsqldb:hsql://localhost:1701 . |
|
| --blocking-timeout-seconds | Specifies the maximum time, in seconds, to block while waiting for a connection before throwing an exception. | |
| --conn-pooled-datasource-class | The fully qualified name of the connection pool implementation that holds XA datasource connections. | |
| --idle-timeout-seconds | Specifies the time, in seconds, that a connection may be idle before being closed. | |
| --init-pool-size | Specifies the initial number of connections the pool should hold. | |
| --login-timeout-seconds | The quantity of seconds after which the client thread will be disconnected due to inactivity. | |
| --managed-conn-factory-class | The fully qualified name of the connection factory implementation. | |
| --max-pool-size | The maximum number of connections that may be created in a pool. | |
| --password | The default password used when creating a new connection. | |
| --transaction-type | Type of the transaction. One of XATransaction , NoTransaction , or LocalTransaction . |
|
| --username | Specifies the default user name to be used when creating a new connection. | |
| --xa-datasource-class | The fully qualified name of the javax.sql.XADataSource implementation class. |
|
| --if-not-exists | When true, a duplicate jndi binding will not be created if one with the same name already exists. When false, an attempt to create a duplicate jndi binding results in an error. The option is set to true if the option is specified without a value. | false |
| --datasource-config-properties | Properties for the custom XSDataSource driver. Append a JSON string containing a (name, type, value) tuple to set any property. For example: --datasource-config-properties={'name':'name1','type':'type1','value':'value1'},{'name':'name2','type':'type2','value':'value2'} |
Example Commands:
gfsh>create jndi-binding --name=jndi1 --type=SIMPLE \
--jdbc-driver-class=org.apache.derby.jdbc.EmbeddedDriver \
--connection-url="jdbc:derby:newDB;create=true"
create lucene index
Create a Lucene index. For details on Lucene index creation, see Apache Lucene Integration.
For additional Lucene-related gfsh commands, see describe lucene index, destroy lucene index, list lucene indexes and search lucene.
Availability: Online. You must be connected in gfsh to a JMX Manager member to use this command.
Syntax:
create lucene index --name=value --region=value --field=value(,value)*
[--analyzer=value(,value)*] [--serializer=value] [--group=value(,value)*]
Parameters, create lucene index:
| Name | Description | Default |
|---|---|---|
| --name | Required. Name of the index to create. | |
| --region | Required. Name/Path of the region on which to define the index. | |
| --field | Required. Field(s) of the region values that are referenced by the index, specified as a comma-separated list. To treat the entire value as a single field, specify __REGION_VALUE_FIELD . |
|
| ‑‑analyzer | Analyzer(s) to extract terms from text, specified as a comma-separated list. If not specified, the default analyzer is used for all fields. If specified, the number of analyzers must exactly match the number of fields specified. When listing analyzers, use the keyword DEFAULT for any field that will use the default analyzer. |
Lucene StandardAnalyzer |
| ‑‑serializer | Fully qualified class name of the serializer to be used with this index. The serializer must implement the LuceneSerializer interface. You can use the built-in org.apache.geode.cache.lucene.FlatFormatSerializer to index and search collections and nested fields. If not specified, the simple default serializer is used, which indexes and searches only the top level fields of the region objects. |
simple serializer |
| --group | The index will be created on all the members in the specified member groups. |
Example Commands:
gfsh>create lucene index --name=customerIndex --region=/Customer
--field=__REGION_VALUE_FIELD
gfsh>create lucene index --name=analyzerIndex --region=/Person
--field=name,email,address,revenue
--analyzer=DEFAULT,org.apache.lucene.analysis.core.KeywordAnalyzer,
examples.MyCharacterAnalyzer,DEFAULT
Sample Output:
gfsh>create lucene index --name=testIndex --region=testRegion
--field=__REGION_VALUE_FIELD
Member | Status
-------------------------------------- | ---------------------------------
<IP_ADDRESS>(server505:17200)<v1>:1025 | Successfully created lucene index
create region
Create a region with given path and configuration.
You must specify either a --type
or a --template-region
for initial configuration when creating a region. Specifying a --key-constraint
and --value-constraint
makes object type information available during querying and indexing.
See Region Data Storage and Distribution.
See Specifying JSON within Command-Line Options for syntax details.
Availability: Online. You must be connected in gfsh
to a JMX Manager member to use this command.
Syntax:
create region --name=value [--type=value] [--template-region=value]
[--groups=value(,value)*] [--if-not-exists(=value)?]
[--key-constraint=value] [--value-constraint=value]
[--enable-statistics=value] [--entry-idle-time-expiration=value]
[--entry-idle-time-expiration-action=value]
[--entry-time-to-live-expiration=value]
[--entry-time-to-live-expiration-action=value]
[--entry-idle-time-custom-expiry=value] [--entry-time-to-live-custom-expiry=value]
[--region-idle-time-expiration=value]
[--region-idle-time-expiration-action=value]
[--region-time-to-live-expiration=value]
[--region-time-to-live-expiration-action=value] [--disk-store=value]
[--enable-synchronous-disk=value] [--enable-async-conflation=value]
[--enable-subscription-conflation=value] [--cache-listener=value(,value)*]
[--cache-loader=value] [--cache-writer=value]
[--async-event-queue-id=value(,value)*]
[--gateway-sender-id=value(,value)*] [--enable-concurrency-checks=value]
[--enable-cloning=value] [--concurrency-level=value]
[--colocated-with=value] [--local-max-memory=value]
[--recovery-delay=value] [--redundant-copies=value]
[--startup-recovery-delay=value] [--total-max-memory=value]
[--total-num-buckets=value] [--compressor=value] [--off-heap(=value)]
[--partition-resolver=value] [--eviction-entry-count=value]
[--eviction-max-memory=value] [--eviction-action=value] [--eviction-object-sizer=value]
Parameters, create region:
| Name | Description | Default | ||||
|---|---|---|---|---|---|---|
| --name | Required. Name/Path of the region to be created. | |||||
| --type | Required (if template-region is not specified.) Type of region to create. Options include: PARTITION, PARTITION_REDUNDANT, REPLICATE, LOCAL, etc.
To get a list of of all region type options, add the --type parameter and then select the TAB key to display a full list. |
|||||
| --template-region | Required (if type is not specified.) Name/Path of the region whose attributes should be duplicated when creating this region. | |||||
| --groups | Group(s) of members on which the region will be created. | |||||
| --if-not-exists | A new region will not be created if a region with the same name already exists. By default, an attempt to create a duplicate region is reported as an error.
If this option is specified without a value or is specified with a value of true , then gfsh displays a “Skipping…” acknowledgement, but does not throw an error. |
false | ||||
| --key-constraint | Fully qualified class name of the objects allowed as region keys. Ensures that keys for region entries are all of the same class. | |||||
| --value-constraint | Fully qualified class name of the objects allowed as region values. If not specified, then region values can be of any class. | |||||
| --enable-statistics | Whether to gather statistics for the region. Must be true to use expiration on the region. | |||||
| --entry-idle-time-expiration | How long the region’s entries can remain in the cache without being accessed. | no expiration | ||||
| --entry-idle-time-expiration-action | Action to be taken on an entry that has exceeded the idle expiration. Valid expiration actions include destroy, local-destroy, invalidate (default), local-invalidate. | |||||
| --entry-time-to-live-expiration | How long the region’s entries can remain in the cache without being accessed or updated. The default is no expiration of this type. | no expiration | ||||
| --entry-time-to-live-expiration-action | Action to be taken on an entry that has exceeded the TTL expiration. Valid expiration actions include destroy, local-destroy, invalidate (default), local-invalidate. | |||||
| --entry-idle-time-custom-expiry | The name of a class implementing CustomExpiry for entry idle time. Append a JSON string for initialization properties. | |||||
| --entry-time-to-live-custom-expiry | The name of a class implementing CustomExpiry for entry time to live. Append a JSON string for initialization properties. | |||||
| --region-idle-time-expiration | How long the region can remain in the cache without being accessed. The default is no expiration of this type. | |||||
| --region-idle-time-expiration-action | Action to be taken on a region that has exceeded the idle expiration. Valid expiration actions include destroy, local-destroy, invalidate (default), local-invalidate. | |||||
| --region-time-to-live-expiration | How long the region can remain in the cache without being accessed or updated. The default is no expiration of this type. | no expiration | ||||
| --region-time-to-live-expiration-action | Action to be taken on a region that has exceeded the TTL expiration. Valid expiration actions include destroy, local-destroy, invalidate (default), local-invalidate. | |||||
| --disk-store | Disk Store to be used by this region. The list disk-stores command can be used to display existing disk stores. | |||||
| --enable-synchronous-disk | Whether writes are done synchronously for regions that persist data to disk. | |||||
| --enable-async-conflation | Whether to allow aggregation of asynchronous TCP/IP messages sent by the producer member of the region. A false value causes all asynchronous messages to be sent individually. | |||||
| --enable-subscription-conflation | Whether the server should conflate its messages to the client. A false value causes all server-client messages to be sent individually. | |||||
| --cache-listener | Fully qualified class name of a plug-in to be instantiated for receiving after-event notification of changes to the region and its entries. Any number of cache listeners can be configured. A fully qualified class name may be appended with a JSON specification that will be parsed to become the fields of the parameter to the init() method for a class that implements the Declarable interface. |
|||||
| --cache-loader | Fully qualified class name of a plug-in to be instantiated for receiving notification of cache misses in the region. At most, one cache loader can be defined in each member for the region. For distributed regions, a cache loader may be invoked remotely from other members that have the region defined. A fully qualified class name may be appended with a JSON specification that will be parsed to become the fields of the parameter to the init() method for a class that implements the Declarable interface. |
|||||
| --cache-writer | Fully qualified class name of a plug-in to be instantiated for receiving before-event notification of changes to the region and its entries. The plug-in may cancel the event. At most, one cache writer can be defined in each member for the region. A fully qualified class name may be appended with a JSON specification that will be parsed to become the fields of the parameter to the init() method for a class that implements the Declarable interface. |
|||||
| --async-event-queue-id | IDs of the Async Event Queues that will be used for write-behind operations. | |||||
| --gateway-sender-id | IDs of the Gateway Senders to which data will be routed. | |||||
| --enable-concurrency-checks | Whether Region Version Vectors are implemented. Region Version Vectors are an extension to the versioning scheme that aid in synchronization of replicated regions. | |||||
| --enable-cloning | Determines how fromDelta applies deltas to the local cache for delta propagation. When true, the updates are applied to a clone of the value and then the clone is saved to the cache. When false, the value is modified in place in the cache. | |||||
| --concurrency-level | Estimate of the maximum number of application threads that will concurrently access a region entry at one time. This attribute does not apply to partitioned regions. | |||||
| --colocated-with | Central Region with which this region should be colocated. | |||||
| --local-max-memory | Maximum amount of memory, in megabytes, to be used by the region in this process. (The default is 90% of available heap.) | |||||
| --recovery-delay | Delay in milliseconds that existing members will wait after a member crashes before restoring this region’s redundancy on the remaining members. The default value (-1) indicates that redundancy will not be recovered after a failure. | |||||
| --redundant-copies | Number of extra copies of buckets desired. Extra copies allow for both high availability in the face of VM departure (intended or unintended) and load balancing read operations. (Allowed values: 0, 1, 2 and 3) | |||||
| --startup-recovery-delay | Delay in milliseconds that new members will wait before assuming their share of cluster-level redundancy. This allows time for multiple regions to start before the redundancy workload is parceled out to the new members. A value of -1 indicates that adding new members will not trigger redundancy recovery. | The default is to recover redundancy immediately when a new member is added. | ||||
| --total-max-memory | Maximum amount of memory, in megabytes, to be used by the region in all processes. | |||||
| --total-num-buckets | Total number of hash buckets to be used by the region in all processes. | 113 | ||||
| --compressor | Java class name that implements compression for the region. You can write a custom compressor that implements org.apache.geode.compression.Compressor or you can specify the Snappy compressor (org.apache.geode.compression.SnappyCompressor ), which is bundled with Geode. See Region Compression. |
no compression | ||||
| --off-heap | Specifies whether the region values are stored in heap memory or off-heap memory. When true, region values are in off-heap memory. If the parameter is specified without a value, the value of true is used. | false | ||||
| --partition-resolver | Specifies the full path to a custom partition resolver. Specify org.apache.geode.cache.util.StringPrefixPartitionResolver to use the included string prefix partition resolver. |
|||||
| --eviction-entry-count | Enables eviction, where the eviction policy is based on the number of entries in the region. | |||||
| --eviction-max-memory | Enables eviction, where the eviction policy is based on the amount of memory consumed by the region, specified in megabytes. | |||||
| --eviction-action | Action to take when the eviction threshold is reached.
|
|||||
| –eviction-object-sizer | Specifies your implementation of the ObjectSizer interface to measure the size of objects in the region. The sizer applies only to heap and memory based eviction. |
Example Commands:
create region --name=region1 --type=REPLICATE_PERSISTENT \
--cache-writer=org.apache.geode.examples.MyCacheWriter \
--group=Group1 --disk-store=DiskStore1
create region --name=region12 --template-region=/region1
create region --name=region2 --type=REPLICATE \
--cache-listener=org.apache.geode.examples.MyCacheListener1,\
org.apache.geode.examples.MyCacheListener2 \
--group=Group1,Group2
create region --name=region3 --type=PARTITION_PERSISTENT --redundant-copies=2 \
--total-max-memory=1000 --startup-recovery-delay=5 --total-num-buckets=100 \
--disk-store=DiskStore2 --cache-listener=org.apache.geode.examples.MyCacheListener3 \
--group=Group2
create region --name=region4 --type=REPLICATE_PROXY \
--cache-listener=org.apache.geode.examples.MyCacheListener1 --group=Group1,Group2
create region --name=myRegion --type=REPLICATE --eviction-max-memory=100 \
--eviction-action=overflow-to-disk --eviction-object-sizer=my.company.geode.MySizer
Sample Output:
gfsh>create region --name=myRegion --type=LOCAL
Member | Status
------- | ---------------------------------------
server1 | Region "/myRegion" created on "server1"


term_stats.Rd
Tokenize a set of texts and tabulate the term occurrence statistics.
term_stats(x, filter = NULL, ngrams = NULL, min_count = NULL, max_count = NULL, min_support = NULL, max_support = NULL, types = FALSE, subset, ...)
| x | a text vector to tokenize. |
|---|---|
| filter | if non- |
| ngrams | an integer vector of n-gram lengths to include, or
|
| min_count | a numeric scalar giving the minimum term count to include
in the output, or |
| max_count | a numeric scalar giving the maximum term count to include
in the output, or |
| min_support | a numeric scalar giving the minimum term support to
include in the output, or |
| max_support | a numeric scalar giving the maximum term support to
include in the output, or |
| types | a logical value indicating whether to include columns for the types that make up the terms. |
| subset | logical expression indicating elements or rows to keep: missing values are taken as false. |
| ... | additional properties to set on the text filter. |
term_stats
tokenizes a set of texts and computes the occurrence
counts and supports for each term. The ‘count’ is the number of
occurrences of the term across all texts; the ‘support’ is the
number of texts containing the term. Each appearance of a term
increments its count by one. Likewise, an appearance of a term in text
i
increments its support once, not for each occurrence
in the text.
To include multi-type terms, specify the designed term lengths using
the ngrams
argument.
A data frame with columns named term
, count
, and
support
, with one row for each appearing term. Rows are sorted
in descending order according to support
and then count
,
with ties broken lexicographically by term
, using the
character ordering determined by the current locale
(see Comparison
for details).
If types = TRUE
, then the result also includes columns named
type1
, type2
, etc. for the types that make up the
term.
term_stats("A rose is a rose is a rose.")#> term count support #> 1 a 3 1 #> 2 rose 3 1 #> 3 is 2 1 #> 4 . 1 1# remove punctuation and English stop words term_stats("A rose is a rose is a rose.", text_filter(drop_symbol = TRUE, drop = stopwords_en))#> term count support #> 1 rose 3 1 #> 2 . 1 1# unigrams, bigrams, and trigrams term_stats("A rose is a rose is a rose.", ngrams = 1:3)#> term count support #> 1 a 3 1 #> 2 a rose 3 1 #> 3 rose 3 1 #> 4 a rose is 2 1 #> 5 is 2 1 #> 6 is a 2 1 #> 7 is a rose 2 1 #> 8 rose is 2 1 #> 9 rose is a 2 1 #> 10 . 1 1 #> 11 a rose . 1 1 #> 12 rose . 1 1# also include the type information term_stats("A rose is a rose is a rose.", ngrams = 1:3, types = TRUE)#> term type1 type2 type3 count support #> 1 a a <NA> <NA> 3 1 #> 2 a rose a rose <NA> 3 1 #> 3 rose rose <NA> <NA> 3 1 #> 4 a rose is a rose is 2 1 #> 5 is is <NA> <NA> 2 1 #> 6 is a is a <NA> 2 1 #> 7 is a rose is a rose 2 1 #> 8 rose is rose is <NA> 2 1 #> 9 rose is a rose is a 2 1 #> 10 . . <NA> <NA> 1 1 #> 11 a rose . a rose . 1 1 #> 12 rose . rose . <NA> 1 1


{% extends "base.html" %} {% load bootstrap4 %} {% block title %}
CREATE YOUR ACCOUNT Welcome to the home of your favourite delicious spicies


How to be Careful with Covid-19 Counts
Rex W. Douglass
1
Executive Summary
1.1
Key Takeaways (TLDR)
2
Global COVID-19 Count Data
2.1
Takeaways
2.2
Unit of Analysis and Definition of Measurements
2.3
Fist Order Cleaning Steps
2.4
Sources of Data
2.5
What is their geographic coverage?
2.5.1
Country Level Data Availability
2.5.2
State/Province Level Data Availability
2.5.3
County District Level Data Availability
2.6
What is their temporal coverage?
2.6.1
Coverage Across Three Countries
2.7
Where and How do they Disagree?
3
Tests
3.1
Tested People versus Tested Samples
3.2
Interpolate Within Observed
3.3
Interplate Prior to Observed
3.4
Interpolate Subnationally
3.5
Explaining Variation in Testing
4
Common Measures of Interest
4.1
R0 and R
4.2
Case Fatality Rate (CFR)
4.3
Percent Positive
5
Deaths
6
Actual Infections
7
Conclusion
References
How to be Careful with Covid-19 Counts: A Practical Guide to Working with Pandemic Panel Data
Chapter 5
Deaths


Spark Standalone Mode
- Security
- Installing Spark Standalone to a Cluster
- Starting a Cluster Manually
- Cluster Launch Scripts
- Connecting an Application to the Cluster
- Launching Spark Applications
- Resource Scheduling
- Executors Scheduling
- Monitoring and Logging
- Running Alongside Hadoop
- Configuring Ports for Network Security
- High Availability
In addition to running on the Mesos or YARN cluster managers, Spark also provides a simple standalone deploy mode. You can launch a standalone cluster either manually, by starting a master and workers by hand, or use our provided launch scripts. It is also possible to run these daemons on a single machine for testing.
Security
Security in Spark is OFF by default. This could mean you are vulnerable to attack by default. Please see Spark Security and the specific security sections in this doc before running Spark.
Installing Spark Standalone to a Cluster
To install Spark Standalone mode, you simply place a compiled version of Spark on each node on the cluster. You can obtain pre-built versions of Spark with each release or build it yourself.
Starting a Cluster Manually
You can start a standalone master server by executing:
./sbin/start-master.sh
Once started, the master will print out a spark://HOST:PORT
URL for itself, which you can use to connect workers to it,
or pass as the “master” argument to SparkContext
. You can also find this URL on
the master’s web UI, which is http://localhost:8080 by default.
Similarly, you can start one or more workers and connect them to the master via:
./sbin/start-slave.sh <master-spark-URL>
Once you have started a worker, look at the master’s web UI (http://localhost:8080 by default). You should see the new node listed there, along with its number of CPUs and memory (minus one gigabyte left for the OS).
Finally, the following configuration options can be passed to the master and worker:
| Argument | Meaning |
|---|---|
-h HOST , --host HOST |
Hostname to listen on |
-i HOST , --ip HOST |
Hostname to listen on (deprecated, use -h or --host) |
-p PORT , --port PORT |
Port for service to listen on (default: 7077 for master, random for worker) |
--webui-port PORT |
Port for web UI (default: 8080 for master, 8081 for worker) |
-c CORES , --cores CORES |
Total CPU cores to allow Spark applications to use on the machine (default: all available); only on worker |
-m MEM , --memory MEM |
Total amount of memory to allow Spark applications to use on the machine, in a format like 1000M or 2G (default: your machine's total RAM minus 1 GB); only on worker |
-d DIR , --work-dir DIR |
Directory to use for scratch space and job output logs (default: SPARK_HOME/work); only on worker |
--properties-file FILE |
Path to a custom Spark properties file to load (default: conf/spark-defaults.conf) |
Cluster Launch Scripts
To launch a Spark standalone cluster with the launch scripts, you should create a file called conf/slaves in your Spark directory, which must contain the hostnames of all the machines where you intend to start Spark workers, one per line. If conf/slaves does not exist, the launch scripts defaults to a single machine (localhost), which is useful for testing. Note, the master machine accesses each of the worker machines via ssh. By default, ssh is run in parallel and requires password-less (using a private key) access to be setup. If you do not have a password-less setup, you can set the environment variable SPARK_SSH_FOREGROUND and serially provide a password for each worker.
Once you’ve set up this file, you can launch or stop your cluster with the following shell scripts, based on Hadoop’s deploy scripts, and available in SPARK_HOME/sbin
:
sbin/start-master.sh
- Starts a master instance on the machine the script is executed on.sbin/start-slaves.sh
- Starts a slave instance on each machine specified in theconf/slaves
file.sbin/start-slave.sh
- Starts a slave instance on the machine the script is executed on.sbin/start-all.sh
- Starts both a master and a number of slaves as described above.sbin/stop-master.sh
- Stops the master that was started via thesbin/start-master.sh
script.sbin/stop-slaves.sh
- Stops all slave instances on the machines specified in theconf/slaves
file.sbin/stop-all.sh
- Stops both the master and the slaves as described above.
Note that these scripts must be executed on the machine you want to run the Spark master on, not your local machine.
You can optionally configure the cluster further by setting environment variables in conf/spark-env.sh
. Create this file by starting with the conf/spark-env.sh.template
, and copy it to all your worker machines for the settings to take effect. The following settings are available:
| Environment Variable | Meaning |
|---|---|
SPARK_MASTER_HOST |
Bind the master to a specific hostname or IP address, for example a public one. |
SPARK_MASTER_PORT |
Start the master on a different port (default: 7077). |
SPARK_MASTER_WEBUI_PORT |
Port for the master web UI (default: 8080). |
SPARK_MASTER_OPTS |
Configuration properties that apply only to the master in the form "-Dx=y" (default: none). See below for a list of possible options. |
SPARK_LOCAL_DIRS |
Directory to use for "scratch" space in Spark, including map output files and RDDs that get stored on disk. This should be on a fast, local disk in your system. It can also be a comma-separated list of multiple directories on different disks. |
SPARK_WORKER_CORES |
Total number of cores to allow Spark applications to use on the machine (default: all available cores). |
SPARK_WORKER_MEMORY |
Total amount of memory to allow Spark applications to use on the machine, e.g. 1000m , 2g (default: total memory minus 1 GB); note that each application's individual memory is configured using its spark.executor.memory property. |
SPARK_WORKER_PORT |
Start the Spark worker on a specific port (default: random). |
SPARK_WORKER_WEBUI_PORT |
Port for the worker web UI (default: 8081). |
SPARK_WORKER_DIR |
Directory to run applications in, which will include both logs and scratch space (default: SPARK_HOME/work). |
SPARK_WORKER_OPTS |
Configuration properties that apply only to the worker in the form "-Dx=y" (default: none). See below for a list of possible options. |
SPARK_DAEMON_MEMORY |
Memory to allocate to the Spark master and worker daemons themselves (default: 1g). |
SPARK_DAEMON_JAVA_OPTS |
JVM options for the Spark master and worker daemons themselves in the form "-Dx=y" (default: none). |
SPARK_DAEMON_CLASSPATH |
Classpath for the Spark master and worker daemons themselves (default: none). |
SPARK_PUBLIC_DNS |
The public DNS name of the Spark master and workers (default: none). |
Note: The launch scripts do not currently support Windows. To run a Spark cluster on Windows, start the master and workers by hand.
SPARK_MASTER_OPTS supports the following system properties:
| Property Name | Default | Meaning |
|---|---|---|
spark.deploy.retainedApplications |
200 |
The maximum number of completed applications to display. Older applications will be dropped from the UI to maintain this limit. |
spark.deploy.retainedDrivers |
200 |
The maximum number of completed drivers to display. Older drivers will be dropped from the UI to maintain this limit. |
spark.deploy.spreadOut |
true |
Whether the standalone cluster manager should spread applications out across nodes or try
to consolidate them onto as few nodes as possible. Spreading out is usually better for
data locality in HDFS, but consolidating is more efficient for compute-intensive workloads. |
spark.deploy.defaultCores |
(infinite) |
Default number of cores to give to applications in Spark's standalone mode if they don't
set spark.cores.max . If not set, applications always get all available
cores unless they configure spark.cores.max themselves.
Set this lower on a shared cluster to prevent users from grabbing
the whole cluster by default. |
spark.deploy.maxExecutorRetries |
10 |
Limit on the maximum number of back-to-back executor failures that can occur before the
standalone cluster manager removes a faulty application. An application will never be removed
if it has any running executors. If an application experiences more than
spark.deploy.maxExecutorRetries failures in a row, no executors
successfully start running in between those failures, and the application has no running
executors then the standalone cluster manager will remove the application and mark it as failed.
To disable this automatic removal, set spark.deploy.maxExecutorRetries to
-1 .
|
spark.worker.timeout |
60 | Number of seconds after which the standalone deploy master considers a worker lost if it receives no heartbeats. |
SPARK_WORKER_OPTS supports the following system properties:
| Property Name | Default | Meaning |
|---|---|---|
spark.worker.cleanup.enabled |
false | Enable periodic cleanup of worker / application directories. Note that this only affects standalone mode, as YARN works differently. Only the directories of stopped applications are cleaned up. |
spark.worker.cleanup.interval |
1800 (30 minutes) | Controls the interval, in seconds, at which the worker cleans up old application work dirs on the local machine. |
spark.worker.cleanup.appDataTtl |
604800 (7 days, 7 * 24 * 3600) | The number of seconds to retain application work directories on each worker. This is a Time To Live and should depend on the amount of available disk space you have. Application logs and jars are downloaded to each application work dir. Over time, the work dirs can quickly fill up disk space, especially if you run jobs very frequently. |
spark.storage.cleanupFilesAfterExecutorExit |
true | Enable cleanup non-shuffle files(such as temp. shuffle blocks, cached RDD/broadcast blocks, spill files, etc) of worker directories following executor exits. Note that this doesn't overlap with `spark.worker.cleanup.enabled`, as this enables cleanup of non-shuffle files in local directories of a dead executor, while `spark.worker.cleanup.enabled` enables cleanup of all files/subdirectories of a stopped and timeout application. This only affects Standalone mode, support of other cluster manangers can be added in the future. |
spark.worker.ui.compressedLogFileLengthCacheSize |
100 | For compressed log files, the uncompressed file can only be computed by uncompressing the files. Spark caches the uncompressed file size of compressed log files. This property controls the cache size. |
Connecting an Application to the Cluster
To run an application on the Spark cluster, simply pass the spark://IP:PORT
URL of the master as to the SparkContext
constructor.
To run an interactive Spark shell against the cluster, run the following command:
./bin/spark-shell --master spark://IP:PORT
You can also pass an option --total-executor-cores <numCores>
to control the number of cores that spark-shell uses on the cluster.
Launching Spark Applications
The spark-submit
script provides the most straightforward way to
submit a compiled Spark application to the cluster. For standalone clusters, Spark currently
supports two deploy modes. In client
mode, the driver is launched in the same process as the
client that submits the application. In cluster
mode, however, the driver is launched from one
of the Worker processes inside the cluster, and the client process exits as soon as it fulfills
its responsibility of submitting the application without waiting for the application to finish.
If your application is launched through Spark submit, then the application jar is automatically
distributed to all worker nodes. For any additional jars that your application depends on, you
should specify them through the --jars
flag using comma as a delimiter (e.g. --jars jar1,jar2
).
To control the application’s configuration or execution environment, see
Spark Configuration.
Additionally, standalone cluster
mode supports restarting your application automatically if it
exited with non-zero exit code. To use this feature, you may pass in the --supervise
flag to
spark-submit
when launching your application. Then, if you wish to kill an application that is
failing repeatedly, you may do so through:
./bin/spark-class org.apache.spark.deploy.Client kill <master url> <driver ID>
You can find the driver ID through the standalone Master web UI at http://<master url>:8080
.
Resource Scheduling
The standalone cluster mode currently only supports a simple FIFO scheduler across applications.
However, to allow multiple concurrent users, you can control the maximum number of resources each
application will use.
By default, it will acquire all cores in the cluster, which only makes sense if you just run one
application at a time. You can cap the number of cores by setting spark.cores.max
in your
SparkConf. For example:
In addition, you can configure spark.deploy.defaultCores
on the cluster master process to change the
default for applications that don’t set spark.cores.max
to something less than infinite.
Do this by adding the following to conf/spark-env.sh
:
This is useful on shared clusters where users might not have configured a maximum number of cores individually.
Executors Scheduling
The number of cores assigned to each executor is configurable. When spark.executor.cores
is
explicitly set, multiple executors from the same application may be launched on the same worker
if the worker has enough cores and memory. Otherwise, each executor grabs all the cores available
on the worker by default, in which case only one executor per application may be launched on each
worker during one single schedule iteration.
Monitoring and Logging
Spark’s standalone mode offers a web-based user interface to monitor the cluster. The master and each worker has its own web UI that shows cluster and job statistics. By default, you can access the web UI for the master at port 8080. The port can be changed either in the configuration file or via command-line options.
In addition, detailed log output for each job is also written to the work directory of each slave node (SPARK_HOME/work
by default). You will see two files for each job, stdout
and stderr
, with all output it wrote to its console.
Running Alongside Hadoop
You can run Spark alongside your existing Hadoop cluster by just launching it as a separate service on the same machines. To access Hadoop data from Spark, just use an hdfs:// URL (typically hdfs://<namenode>:9000/path
, but you can find the right URL on your Hadoop Namenode’s web UI). Alternatively, you can set up a separate cluster for Spark, and still have it access HDFS over the network; this will be slower than disk-local access, but may not be a concern if you are still running in the same local area network (e.g. you place a few Spark machines on each rack that you have Hadoop on).
Configuring Ports for Network Security
Generally speaking, a Spark cluster and its services are not deployed on the public internet. They are generally private services, and should only be accessible within the network of the organization that deploys Spark. Access to the hosts and ports used by Spark services should be limited to origin hosts that need to access the services.
This is particularly important for clusters using the standalone resource manager, as they do not support fine-grained access control in a way that other resource managers do.
For a complete list of ports to configure, see the security page.
High Availability
By default, standalone scheduling clusters are resilient to Worker failures (insofar as Spark itself is resilient to losing work by moving it to other workers). However, the scheduler uses a Master to make scheduling decisions, and this (by default) creates a single point of failure: if the Master crashes, no new applications can be created. In order to circumvent this, we have two high availability schemes, detailed below.
Standby Masters with ZooKeeper
Overview
Utilizing ZooKeeper to provide leader election and some state storage, you can launch multiple Masters in your cluster connected to the same ZooKeeper instance. One will be elected “leader” and the others will remain in standby mode. If the current leader dies, another Master will be elected, recover the old Master’s state, and then resume scheduling. The entire recovery process (from the time the first leader goes down) should take between 1 and 2 minutes. Note that this delay only affects scheduling new applications – applications that were already running during Master failover are unaffected.
Learn more about getting started with ZooKeeper here.
Configuration
In order to enable this recovery mode, you can set SPARK_DAEMON_JAVA_OPTS in spark-env by configuring spark.deploy.recoveryMode
and related spark.deploy.zookeeper.* configurations.
For more information about these configurations please refer to the configuration doc
Possible gotcha: If you have multiple Masters in your cluster but fail to correctly configure the Masters to use ZooKeeper, the Masters will fail to discover each other and think they’re all leaders. This will not lead to a healthy cluster state (as all Masters will schedule independently).
Details
After you have a ZooKeeper cluster set up, enabling high availability is straightforward. Simply start multiple Master processes on different nodes with the same ZooKeeper configuration (ZooKeeper URL and directory). Masters can be added and removed at any time.
In order to schedule new applications or add Workers to the cluster, they need to know the IP address of the current leader. This can be accomplished by simply passing in a list of Masters where you used to pass in a single one. For example, you might start your SparkContext pointing to spark://host1:port1,host2:port2
. This would cause your SparkContext to try registering with both Masters – if host1
goes down, this configuration would still be correct as we’d find the new leader, host2
.
There’s an important distinction to be made between “registering with a Master” and normal operation. When starting up, an application or Worker needs to be able to find and register with the current lead Master. Once it successfully registers, though, it is “in the system” (i.e., stored in ZooKeeper). If failover occurs, the new leader will contact all previously registered applications and Workers to inform them of the change in leadership, so they need not even have known of the existence of the new Master at startup.
Due to this property, new Masters can be created at any time, and the only thing you need to worry about is that new applications and Workers can find it to register with in case it becomes the leader. Once registered, you’re taken care of.
Single-Node Recovery with Local File System
Overview
ZooKeeper is the best way to go for production-level high availability, but if you just want to be able to restart the Master if it goes down, FILESYSTEM mode can take care of it. When applications and Workers register, they have enough state written to the provided directory so that they can be recovered upon a restart of the Master process.
Configuration
In order to enable this recovery mode, you can set SPARK_DAEMON_JAVA_OPTS in spark-env using this configuration:
| System property | Meaning |
|---|---|
spark.deploy.recoveryMode |
Set to FILESYSTEM to enable single-node recovery mode (default: NONE). |
spark.deploy.recoveryDirectory |
The directory in which Spark will store recovery state, accessible from the Master's perspective. |
Details
- This solution can be used in tandem with a process monitor/manager like monit, or just to enable manual recovery via restart.
- While filesystem recovery seems straightforwardly better than not doing any recovery at all, this mode may be suboptimal for certain development or experimental purposes. In particular, killing a master via stop-master.sh does not clean up its recovery state, so whenever you start a new Master, it will enter recovery mode. This could increase the startup time by up to 1 minute if it needs to wait for all previously-registered Workers/clients to timeout.
- While it’s not officially supported, you could mount an NFS directory as the recovery directory. If the original Master node dies completely, you could then start a Master on a different node, which would correctly recover all previously registered Workers/applications (equivalent to ZooKeeper recovery). Future applications will have to be able to find the new Master, however, in order to register.


Preparation time
less than 30 mins
Cooking time
no cooking required
Serves
Serves 4
Complement red mullet with Jun Tanaka’s punchy flavourings.
Place 60g/2½oz hazelnuts in a food processor and blend until finely ground. Mix with the dried breadcrumbs until well combined and pour onto a baking tray.
Brush egg white onto the skin of the fish, then dip into the hazelnut mixture until covered. Season with salt and freshly ground black pepper.
Heat one tablespoon olive oil in a non-stick frying pan and add the fish, skin-side down. Cook for 3-4 minutes, then add the butter. Turn the fish over and cook for a further 2-3 minutes.
Meanwhile, for the 'risotto', bring a large pan of salted water to the boil and add the shredded green part of the leek. Cook for 3-4 minutes, or until soft.
Plunge the leeks into iced water, drain and blend in a food processor until smooth.
Heat a frying pan until hot. Add the butter and shallots and fry for 1 minute. Add the remaining leek and potatoes, and cook for 1 minute.
Add the hot chicken stock and simmer for 5-6minutes, or until the potatoes are tender.
Stir in the leek purée, mascarpone and parmesan. Season to taste with salt and freshly ground black pepper.
For the red wine jus, pour the wine and port into a saucepan. Add the rosemary and heat until the mixture has reduced by half. Pour in the fish stock and heat until the mixture has reduced by three-quarters. Stir in the butter. Season to taste with salt and freshly ground black pepper. And a squeeze of lemon juice.
To serve, spoon the 'risotto' into the centre of a plate. Place two fish fillets on top, drizzle around the red wine jus, and finish with a scattering of the reserved hazelnuts.


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Links to various Careers Websites for Year 11 Enrichment Day - September 2016
- For a discussion of which A-levels are acceptably for which courses at Russell
Group Universities see here
- On this website you can select A level subjects and see which degree courses and careers they could lead to. You can also compare lifetime earnings for various degree courses ( not that you should be so mercenary).
- This link gives career options by degree subject.
- This link and this link have careers profiles on 100's of graduate-level careers.
- This site replaces jobs4u website and has information on careers at all entry levels.
- A link for higher apprenticeships.
- Specific website for engineering careers.
- Website for creative careers including advertising, computer games design, film/TV.
- For information on legal careers.
- For information on mathematics related careers.
- For information on science and engineering careers.


This website was made for Database Management System. Still under construction. MADE BY JUMAR, TANJEEB AND ZAHID
123-456-6789
jumar.alam@northsouth.edu


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7. Computational Performance¶
For some applications the performance (mainly latency and throughput at prediction time) of estimators is crucial. It may also be of interest to consider the training throughput but this is often less important in a production setup (where it often takes place offline).
We will review here the orders of magnitude you can expect from a number of scikit-learn estimators in different contexts and provide some tips and tricks for overcoming performance bottlenecks.
Prediction latency is measured as the elapsed time necessary to make a prediction (e.g. in micro-seconds). Latency is often viewed as a distribution and operations engineers often focus on the latency at a given percentile of this distribution (e.g. the 90 percentile).
Prediction throughput is defined as the number of predictions the software can deliver in a given amount of time (e.g. in predictions per second).
An important aspect of performance optimization is also that it can hurt prediction accuracy. Indeed, simpler models (e.g. linear instead of non-linear, or with fewer parameters) often run faster but are not always able to take into account the same exact properties of the data as more complex ones.
7.1. Prediction Latency¶
One of the most straight-forward concerns one may have when using/choosing a machine learning toolkit is the latency at which predictions can be made in a production environment.
- The main factors that influence the prediction latency are
- Number of features
- Input data representation and sparsity
- Model complexity
- Feature extraction
A last major parameter is also the possibility to do predictions in bulk or one-at-a-time mode.
7.1.1. Bulk versus Atomic mode¶
In general doing predictions in bulk (many instances at the same time) is more efficient for a number of reasons (branching predictability, CPU cache, linear algebra libraries optimizations etc.). Here we see on a setting with few features that independently of estimator choice the bulk mode is always faster, and for some of them by 1 to 2 orders of magnitude:
To benchmark different estimators for your case you can simply change the
n_features
parameter in this example:
Prediction Latency. This should give
you an estimate of the order of magnitude of the prediction latency.
7.1.2. Influence of the Number of Features¶
Obviously when the number of features increases so does the memory consumption of each example. Indeed, for a matrix of instances with features, the space complexity is in . From a computing perspective it also means that the number of basic operations (e.g., multiplications for vector-matrix products in linear models) increases too. Here is a graph of the evolution of the prediction latency with the number of features:
Overall you can expect the prediction time to increase at least linearly with the number of features (non-linear cases can happen depending on the global memory footprint and estimator).
7.1.3. Influence of the Input Data Representation¶
Scipy provides sparse matrix datastructures which are optimized for storing sparse data. The main feature of sparse formats is that you don’t store zeros so if your data is sparse then you use much less memory. A non-zero value in a sparse (CSR or CSC) representation will only take on average one 32bit integer position + the 64 bit floating point value + an additional 32bit per row or column in the matrix. Using sparse input on a dense (or sparse) linear model can speedup prediction by quite a bit as only the non zero valued features impact the dot product and thus the model predictions. Hence if you have 100 non zeros in 1e6 dimensional space, you only need 100 multiply and add operation instead of 1e6.
Calculation over a dense representation, however, may leverage highly optimised vector operations and multithreading in BLAS, and tends to result in fewer CPU cache misses. So the sparsity should typically be quite high (10% non-zeros max, to be checked depending on the hardware) for the sparse input representation to be faster than the dense input representation on a machine with many CPUs and an optimized BLAS implementation.
Here is sample code to test the sparsity of your input:
def sparsity_ratio(X):
return 1.0 - np.count_nonzero(X) / float(X.shape[0] * X.shape[1])
print("input sparsity ratio:", sparsity_ratio(X))
As a rule of thumb you can consider that if the sparsity ratio is greater
than 90% you can probably benefit from sparse formats. Check Scipy’s sparse
matrix formats documentation
for more information on how to build (or convert your data to) sparse matrix
formats. Most of the time the CSR
and CSC
formats work best.
7.1.4. Influence of the Model Complexity¶
Generally speaking, when model complexity increases, predictive power and latency are supposed to increase. Increasing predictive power is usually interesting, but for many applications we would better not increase prediction latency too much. We will now review this idea for different families of supervised models.
For sklearn.linear_model
(e.g. Lasso, ElasticNet,
SGDClassifier/Regressor, Ridge & RidgeClassifier,
PassiveAgressiveClassifier/Regressor, LinearSVC, LogisticRegression...) the
decision function that is applied at prediction time is the same (a dot product)
, so latency should be equivalent.
Here is an example using
sklearn.linear_model.stochastic_gradient.SGDClassifier
with the
elasticnet
penalty. The regularization strength is globally controlled by
the alpha
parameter. With a sufficiently high alpha
,
one can then increase the l1_ratio
parameter of elasticnet
to
enforce various levels of sparsity in the model coefficients. Higher sparsity
here is interpreted as less model complexity as we need fewer coefficients to
describe it fully. Of course sparsity influences in turn the prediction time
as the sparse dot-product takes time roughly proportional to the number of
non-zero coefficients.
For the sklearn.svm
family of algorithms with a non-linear kernel,
the latency is tied to the number of support vectors (the fewer the faster).
Latency and throughput should (asymptotically) grow linearly with the number
of support vectors in a SVC or SVR model. The kernel will also influence the
latency as it is used to compute the projection of the input vector once per
support vector. In the following graph the nu
parameter of
sklearn.svm.classes.NuSVR
was used to influence the number of
support vectors.
For sklearn.ensemble
of trees (e.g. RandomForest, GBT,
ExtraTrees etc) the number of trees and their depth play the most
important role. Latency and throughput should scale linearly with the number
of trees. In this case we used directly the n_estimators
parameter of
sklearn.ensemble.gradient_boosting.GradientBoostingRegressor
.
In any case be warned that decreasing model complexity can hurt accuracy as mentioned above. For instance a non-linearly separable problem can be handled with a speedy linear model but prediction power will very likely suffer in the process.
7.1.5. Feature Extraction Latency¶
Most scikit-learn models are usually pretty fast as they are implemented either with compiled Cython extensions or optimized computing libraries. On the other hand, in many real world applications the feature extraction process (i.e. turning raw data like database rows or network packets into numpy arrays) governs the overall prediction time. For example on the Reuters text classification task the whole preparation (reading and parsing SGML files, tokenizing the text and hashing it into a common vector space) is taking 100 to 500 times more time than the actual prediction code, depending on the chosen model.
In many cases it is thus recommended to carefully time and profile your feature extraction code as it may be a good place to start optimizing when your overall latency is too slow for your application.
7.2. Prediction Throughput¶
Another important metric to care about when sizing production systems is the throughput i.e. the number of predictions you can make in a given amount of time. Here is a benchmark from the Prediction Latency example that measures this quantity for a number of estimators on synthetic data:
These throughputs are achieved on a single process. An obvious way to increase the throughput of your application is to spawn additional instances (usually processes in Python because of the GIL) that share the same model. One might also add machines to spread the load. A detailed explanation on how to achieve this is beyond the scope of this documentation though.
7.3. Tips and Tricks¶
7.3.1. Linear algebra libraries¶
As scikit-learn relies heavily on Numpy/Scipy and linear algebra in general it makes sense to take explicit care of the versions of these libraries. Basically, you ought to make sure that Numpy is built using an optimized BLAS / LAPACK library.
Not all models benefit from optimized BLAS and Lapack implementations. For
instance models based on (randomized) decision trees typically do not rely on
BLAS calls in their inner loops, nor do kernel SVMs (SVC
, SVR
,
NuSVC
, NuSVR
). On the other hand a linear model implemented with a
BLAS DGEMM call (via numpy.dot
) will typically benefit hugely from a tuned
BLAS implementation and lead to orders of magnitude speedup over a
non-optimized BLAS.
You can display the BLAS / LAPACK implementation used by your NumPy / SciPy / scikit-learn install with the following commands:
from numpy.distutils.system_info import get_info
print(get_info('blas_opt'))
print(get_info('lapack_opt'))
- Optimized BLAS / LAPACK implementations include:
- Atlas (need hardware specific tuning by rebuilding on the target machine)
- OpenBLAS
- MKL
- Apple Accelerate and vecLib frameworks (OSX only)
More information can be found on the Scipy install page and in this blog post from Daniel Nouri which has some nice step by step install instructions for Debian / Ubuntu.
Warning
Multithreaded BLAS libraries sometimes conflict with Python’s
multiprocessing
module, which is used by e.g. GridSearchCV
and
most other estimators that take an n_jobs
argument (with the exception
of SGDClassifier
, SGDRegressor
, Perceptron
,
PassiveAggressiveClassifier
and tree-based methods such as random
forests). This is true of Apple’s Accelerate and OpenBLAS when built with
OpenMP support.
Besides scikit-learn, NumPy and SciPy also use BLAS internally, as explained earlier.
If you experience hanging subprocesses with n_jobs>1
or n_jobs=-1
,
make sure you have a single-threaded BLAS library, or set n_jobs=1
,
or upgrade to Python 3.4 which has a new version of multiprocessing
that should be immune to this problem.
7.3.2. Model Compression¶
Model compression in scikit-learn only concerns linear models for the moment. In this context it means that we want to control the model sparsity (i.e. the number of non-zero coordinates in the model vectors). It is generally a good idea to combine model sparsity with sparse input data representation.
Here is sample code that illustrates the use of the sparsify()
method:
clf = SGDRegressor(penalty='elasticnet', l1_ratio=0.25)
clf.fit(X_train, y_train).sparsify()
clf.predict(X_test)
In this example we prefer the elasticnet
penalty as it is often a good
compromise between model compactness and prediction power. One can also
further tune the l1_ratio
parameter (in combination with the
regularization strength alpha
) to control this tradeoff.
A typical benchmark on synthetic data yields a >30% decrease in latency when both the model and input are sparse (with 0.000024 and 0.027400 non-zero coefficients ratio respectively). Your mileage may vary depending on the sparsity and size of your data and model. Furthermore, sparsifying can be very useful to reduce the memory usage of predictive models deployed on production servers.
7.3.3. Model Reshaping¶
Model reshaping consists in selecting only a portion of the available features
to fit a model. In other words, if a model discards features during the
learning phase we can then strip those from the input. This has several
benefits. Firstly it reduces memory (and therefore time) overhead of the
model itself. It also allows to discard explicit
feature selection components in a pipeline once we know which features to
keep from a previous run. Finally, it can help reduce processing time and I/O
usage upstream in the data access and feature extraction layers by not
collecting and building features that are discarded by the model. For instance
if the raw data come from a database, it can make it possible to write simpler
and faster queries or reduce I/O usage by making the queries return lighter
records.
At the moment, reshaping needs to be performed manually in scikit-learn.
In the case of sparse input (particularly in CSR
format), it is generally
sufficient to not generate the relevant features, leaving their columns empty.


- make(double[], int, int) - Static method in class com.teamten.math.Matrix
-
Public factory that copies its input array.
- make(double...) - Static method in class com.teamten.math.Vector
-
Public factory that copies its input array.
- make4x4(double...) - Static method in class com.teamten.math.Matrix
-
Make a 4x4 matrix with the specified numbers in row-major order.
- makeTranslation(Vector) - Static method in class com.teamten.math.Matrix
-
Make a 4x4 translation matrix with the given translation.
- makeUnit(int) - Static method in class com.teamten.math.Matrix
-
Make a unit sizexsize matrix.
- makeWithBasis(Vector, Vector, Vector) - Static method in class com.teamten.math.Matrix
-
Make a 4x4 matrix with the three vectors as the basis vectors:
x is the first row, y the second row, and z the third row.
- Matrix - Class in com.teamten.math
-
Immutable matrix class.
- modulus() - Method in class com.teamten.math.Complex
-
Returns the modulus of this complex number, i.e., the distance from
the number to the complex origin.
- MS_PER_S - Static variable in class com.teamten.util.Dates
-
Number of milliseconds per second.
- multiply(Complex) - Method in class com.teamten.math.Complex
-
Multiplies complex numbers.
- multiply(double) - Method in class com.teamten.math.Complex
-
Multiplies a complex number with a scalar.
- multiply(double) - Method in class com.teamten.math.Matrix
-
Multiplies a matrix and a scalar.
- multiply(Matrix) - Method in class com.teamten.math.Matrix
-
Multiplies two matrices.
- multiply(double) - Method in class com.teamten.math.Vector
-
Multiplies a vector and a scalar.
- toString() - Method in class com.teamten.math.Matrix
-
Returns a string like "|1,2,3|\n|4,5,6|\n|7,8,9|" with no newline at the
end.
- toString(String) - Method in class com.teamten.math.Matrix
-
Returns a string like "|1,2,3|\n|4,5,6|\n|7,8,9|" with "indent" at the
front of each line and no newline at the end.
- toString() - Method in class com.teamten.math.Vector
-
Returns a string like "[1,2,3]".
- toString(int) - Static method in class com.teamten.util.CodePoints
-
Simple routine to convert a single code point to a string, because surprisingly this isn't a thing
in the standard library.
- toString(int) - Static method in class com.teamten.util.RomanNumerals
-
Converts a number to a Roman numeral in lower case.
- transform(Vector) - Method in class com.teamten.math.Matrix
-
Transforms the vector by the matrix.
- transformVector(Vector) - Method in class com.teamten.math.Matrix
-
Transforms a vector (rather than a point).


To show badge add .nui-badge-counter
class to the element with position: relative
CSS rule.
To display desired content, pass a string to the data-before
attribute on the element where Badge class applied.
To show badge without content use .nui-badge-empty
class to the element with position: relative
CSS rule.
Badge provides classes with all system colors that NovaUI uses.
Badge provides class for change color text to black, use .nui-badge-text-black
for proper contrast between text and background.
Badge is opened for customization, for this, declare CSS class with :after
psuedo-element and apply it to the element.
For some NovaUI elements undesirable applying badge classes directly, because badge won't be shown. In this case, wrap NovaUI
element for example in div
, and apply CSS Badge related classes to this wrapper. See nui-textbox
example.


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With combined experience of over 10 years, our technical team can assist you in creating world class applications that drive sales. We have experience in design, API integrations, Web applications, Databases, you name it! We are a team of technologists who truly love solving problems. We will help you analyze and architect solutions for your business that bring value to your customers.
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import {LinkedList} from<EMAIL_ADDRESS>LinkedList
LinkedList with superpowers! 💪
Constructor Summary
| Private Constructor | ||
| private |
|
Member Summary
| Public Members | ||
| public get |
size: number: * Get the size of the list |
|
| public get |
Get the tail of the list |
| Private Members | ||
| private |
_head: * |
|
| private |
_size: * |
|
| private |
_tail: * |
|
| private get |
head: * |
Method Summary
| Public Methods | ||
| public |
append(value: Item): number Append a node to the rear of the list |
|
| public |
insert(position: number, value: Item): number Insert a node at a given position |
|
| public |
pop(): Item Pop a node from the rear of list |
|
| public |
prepend(value: Item): number Prepend a node to the front of the list |
|
| public |
push(value: Item): number Push a node to the rear of the list |
|
| public |
remove(position: number): Item Remove a node at a given position |
|
| public |
shift(): Item Shift a node from the front of list |
|
| public |
toArray(): Array<Item> Convert the node and next nodes (recursively) to an array |
|
| public |
unshift(value: Item): number Unshift a node to the front of the list |
Private Constructors
private constructor() source
Public Members
public get size: number: * source
Get the size of the list
Return:
| number | number of nodes in the list |
Private Members
private _head: * source
private _size: * source
private _tail: * source
private get head: * source
Public Methods
public append(value: Item): number source
Append a node to the rear of the list
Params:
| Name | Type | Attribute | Description |
| value | Item | value to append to list |
Return:
| number | size after insertion |
public insert(position: number, value: Item): number source
Insert a node at a given position
Params:
| Name | Type | Attribute | Description |
| position | number | position to insert node |
|
| value | Item | value to insert into list |
Return:
| number | size after insertion |
public prepend(value: Item): number source
Prepend a node to the front of the list
Params:
| Name | Type | Attribute | Description |
| value | Item | value to prepend to list |
Return:
| number | size after insertion |
public push(value: Item): number source
Push a node to the rear of the list
Params:
| Name | Type | Attribute | Description |
| value | Item | value to push to list |
Return:
| number | size after insertion |
public remove(position: number): Item source
Remove a node at a given position
Params:
| Name | Type | Attribute | Description |
| position | number | position to remove node |
Return:
| Item | removed item |
public toArray(): Array<Item> source
Convert the node and next nodes (recursively) to an array
Return:
| Array<Item> | array representation of the list |
public unshift(value: Item): number source
Unshift a node to the front of the list
Params:
| Name | Type | Attribute | Description |
| value | Item | value to unshift to list |
Return:
| number | size after insertion |


You can read our API Instructions and develop it too. We recommend postman or another program to get datas (f.e: curl)
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Telephone: 0850 000 00 00
Mail<EMAIL_ADDRESS>Address: Turkey


lamvi - Language Model Visual Inspector
Config:
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july 24, 2015
Ruby reminds me of a girl who's playing hard to get: mysterious, sensible, particular (she wants what she wants and won't settle for less!). You've got to put in the effort if you want to win her over, but boy when you do, having her on your arm (or in the case of this terrible analogy, on your resume) makes it all worth it. This week I began to get to know Ruby, starting with a few of her classes for storing data: arrays and hashes.
An array is essentially a list. It can hold all kinds of objects--strings, integers, floating numbers, even other arrays (arrayception!)
Let's pretend, hypothetically, that I'm a cat lady. No truth to this whatsoever, just an example. I have so many cats, I need a list to keep track of all their names. An array would be perfect for this task.
The contents of an array are ordered and indexed by number. The first object is 0, the second is 1, the third 2, all the way down the list. You can access an object by specifying its position in the array, like this:
A hash is similar to an array, but it stores data in pairs. One half of the pair is the key, the other half, the value. Some find it helpful to think of a hash like a dictionary: the key is the word you're looking up, and the value is its definition.
Let's pretend (again, hypothetically) that I have so many cats, I can't even remember who is who. I need a cheat sheet that gives me a description of the cat when I enter its name. This is where a hash comes in handy.
In this case, the hash key (on the left) is the cat's name, and the value (on the right) is the cat's description.
If you think about it, an array is basically a hash, but with a fixed key--the integer defining object placement is the key. And a hash is basically an array whose key can be anything, not just a number. The key is key to your understanding!
Arrays and hashes are both collections of information. Arrays are like lists, and hashes allow for more details by storing objects in pairs. Also, I totally lied. I am a cat lady. Just the one, though. No need for an array or hash to keep them straight. Yet.


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Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged.
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived.
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived.
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived.
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jQuery-HighLight-Twitter-Plugin QUnit Tests
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I started out CSS week in February with a spirit of confidence and ended it reduced to a puddle of nerves. Positioning was my great downfall. Despite hours of trying, I was unable to position the main portion of my blog index page correctly. It kept showing up below the element on the left, a navigation bar. And because my blog pages use the same basic formatting as my blog index, all of those pages were affected as well
I am a high achiever. I am unused to failure in a learning environment. The inability to achieve a seemingly simple task was a huge blow to my ego. I ended up having to walk away with a broken website and a fear of CSS.
That fear kept me from doing as much as I wanted to my site. Had I not been running to keep up with the other work for Dev Bootcamp, I would say I avoided working on my website. But the truth is that I focused on Ruby and learning the logic of the programming languages and tried to push the embarassment my site represented for me to the back of my mind.
After poking around a bit the last several weeks, this week was the first one that I really tried to address my site again. Still, in order to be able to cope, I had to have my hand held. A bootcamp colleague took a look at my HTML and CSS code, tried a few things and was likewise mystified as to why this area kept getting pushed down below. Then when comparing the working index page with the broken blog index page, she noticed a discrepancy - my index page used a "section" tag, whereas my blog index page used a "main" tag.
She changed the "main" tag to a "section" tag and, lo and behold, that part was tranported into place next to the nav bar, as I had planned 6 weeks before! My CSS positioning wasn't the problem in the least! In an effort to use HTML5 tags, I chose "main", since I believed it to be descriptive of my section. What I didn't realize and hadn't found out onis that it either takes up the page width or must be flush left.
My friend's discovery and other helpful advice gave me the heart I needed to put more work into my site and improve its appearance and functionality and I am exceedingly grateful. Now, even though my site is far from perfect, it is dramatically better than it was. Even more importantly, I have gained the requisite courage to work with HTML and CSS again without fear and trepidation, which is critical for the intense weeks ahead in Dev Bootcamp.


--- layout: page title: "Welcome" ---
I am a PhD Student in Computer
Science at the University of
Houston (UH). My research interests are in the areas of software engineering, deep neural code intelligence models, big data analytics, security, systems, and information visualization.
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about my projects and experiences.
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RT @undees: My awesome teammate @rachelkleinpdx wrote an official New Relic blog post about welcoming new engineers! 💖 https://t.co/mjDXb4m…
7/4/2018, 9:41:41 AM
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RT @undees: My awesome teammate @rachelkleinpdx wrote an official New Relic blog post about welcoming new engineers! 💖 https://t.co/mjDXb4m…
7/4/2018, 9:41:41 AM
Favs: 0
Retweets: 0
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Soldier Orbyu:
Will you bring the research documents you delivered to War Mage Artius?
Based on the information, he might give you a new mission.
Say yes.


Understanding Computer Network Security Part 2: Wireless Network Poisoning
If you haven't already, checkout my last post regarding wireless network sniffing as it will help set the scene for what's going on here.
Wireless network poisoning is the practice of getting onto a wireless network, and then tricking other users of the network to route all of the traffic to your computer, before it goes to the router, and therefore, before it goes on to the internet. By "user", I mean any device connected to the network such as a phone, laptop, tablet, pc, etc.
As that image comically points out, wireless network signals are sent in all directions, so the attacker actually doesn't need to be in between you and the network, or have physical access to any device.
Now let's pretend I have poisoned a network, and I'm getting all of those sweet sweet 802.11 frames (data). What can I actually do with this?
- I can modify the information that the router returns to you.
- I can take this a step further by tampering with encryption, which is more easily detectable, and easy to defend yourself from.
The first item on the list, we talked about earlier, and the last item we will get to eventually, but the second item on our list is by far the most interesting. When I have all of the traffic running through my computer, I can actually edit what gets sent back to the user.
How is this dangerous? Well what if you were trying to access Facebook, and instead, I sent you a new version of Facebook that has a little bit of code in the background that sends me every keypress you type. This could include any private message, credit card, password, etc.
How do you defend against an attack like this? Well first off, you shouldn't send private information over a public WiFi network. Secondly, ensure that encryption is available on websites that you are browsing.
To learn more about how wireless network poisoning works, learn about the ARP protocol. Also, it would certainly be worth while to take a look at Ettercap.


Creative art and fine art
Art is a diverse range of human activities in creating visual, auditory or performing artifacts (artworks), expressing the author's imaginative or technical skill, intended to be appreciated for their beauty or emotional power.


net.sourceforge.cilib.Measurement
Interfaces
Measurement
MeasurementFactory
Classes
Fitness
FitnessEvaluations
FunctionOptimisationError
Iterations
MultipleFitness
MultipleSolutions
ParticlePositions
PercentageComplete
Restarts
Solution
SwarmDiameter
Time


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