Abstract: A field extraction template simplifies the creation of field extraction rules by providing a user with a set of field names commonly assigned to a certain type of data, as well as guidance on how to extract values for those fields. These field extraction rules, in turn, facilitate access to certain “chunks” of the data, or to information derived from those chunks, through named fields. A field extraction template comprises at least a set of field names and ordering data for the field names. The ordering data indicates index positions that are associated with at least some of the field names. A delimiter is specified for splitting data items into arrays of chunks. The chunk of a data item that belongs to a given field name is the chunk whose position within the item's array of chunks is equivalent to the index position associated with the given field name.

Abstract: A field extraction template simplifies the creation of field extraction rules by providing a user with a set of field names commonly assigned to a certain type of data, as well as guidance on how to extract values for those fields. These field extraction rules, in turn, facilitate access to certain “chunks” of the data, or to information derived from those chunks, through named fields. A field extraction template comprises at least a set of field names and ordering data for the field names. The ordering data indicates index positions that are associated with at least some of the field names. A delimiter is specified for splitting data items into arrays of chunks. The chunk of a data item that belongs to a given field name is the chunk whose position within the item's array of chunks is equivalent to the index position associated with the given field name.

Abstract: A field extraction template simplifies the creation of field extraction rules by providing a user with a set of field names commonly assigned to a certain type of data, as well as guidance on how to extract values for those fields. These field extraction rules, in turn, facilitate access to certain “chunks” of the data, or to information derived from those chunks, through named fields. A field extraction template comprises at least a set of field names and ordering data for the field names. The ordering data indicates index positions that are associated with at least some of the field names. A delimiter is specified for splitting data items into arrays of chunks. The chunk of a data item that belongs to a given field name is the chunk whose position within the item's array of chunks is equivalent to the index position associated with the given field name.

Abstract: Embodiments are directed towards the visualization of machine data received from computing clusters. Embodiments may enable improved analysis of computing cluster performance, error detection, troubleshooting, error prediction, or the like. Individual cluster nodes may generate machine data that includes information and data regarding the operation and status of the cluster node. The machine data is received from each cluster node for indexing by one or more indexing applications. The indexed machine data including the complete data set may be stored in one or more index stores. A visualization application enables a user to select one or more analysis lenses that may be used to generate visualizations of the machine data. The visualization application employs the analysis lens to produce visualizations of the computing cluster machine data.

Abstract: Embodiments are directed towards the visualization of machine data received from computing clusters. Embodiments may enable improved analysis of computing cluster performance, error detection, troubleshooting, error prediction, or the like. Individual cluster nodes may generate machine data that includes information and data regarding the operation and status of the cluster node. The machine data is received from each cluster node for indexing by one or more indexing applications. The indexed machine data including the complete data set may be stored in one or more index stores. A visualization application enables a user to select one or more analysis lenses that may be used to generate visualizations of the machine data. The visualization application employs the analysis lens to produce visualizations of the computing cluster machine data.

Abstract: Embodiments are directed towards the visualization of machine data received from computing clusters. Embodiments may enable improved analysis of computing cluster performance, error detection, troubleshooting, error prediction, or the like. Individual cluster nodes may generate machine data that includes information and data regarding the operation and status of the cluster node. The machine data is received from each cluster node for indexing by one or more indexing applications. The indexed machine data including the complete data set may be stored in one or more index stores. A visualization application enables a user to select one or more analysis lenses that may be used to generate visualizations of the machine data. The visualization application employs the analysis lens to produce visualizations of the computing cluster machine data.

Abstract: A field extraction template simplifies the creation of field extraction rules by providing a user with a set of field names commonly assigned to a certain type of data, as well as guidance on how to extract values for those fields. These field extraction rules, in turn, facilitate access to certain “chunks” of the data, or to information derived from those chunks, through named fields. A field extraction template comprises at least a set of field names and ordering data for the field names. The ordering data indicates index positions that are associated with at least some of the field names. A delimiter is specified for splitting data items into arrays of chunks. The chunk of a data item that belongs to a given field name is the chunk whose position within the item's array of chunks is equivalent to the index position associated with the given field name.

Abstract: A field extraction template simplifies the creation of field extraction rules by providing a user with a set of field names commonly assigned to a certain type of data, as well as guidance on how to extract values for those fields. These field extraction rules, in turn, facilitate access to certain “chunks” of the data, or to information derived from those chunks, through named fields. A field extraction template comprises at least a set of field names and ordering data for the field names. The ordering data indicates index positions that are associated with at least some of the field names. A delimiter is specified for splitting data items into arrays of chunks. The chunk of a data item that belongs to a given field name is the chunk whose position within the item's array of chunks is equivalent to the index position associated with the given field name.

Abstract: Embodiments are directed towards the visualization of machine data received from computing clusters. Embodiments may enable improved analysis of computing cluster performance, error detection, troubleshooting, error prediction, or the like. Individual cluster nodes may generate machine data that includes information and data regarding the operation and status of the cluster node. The machine data is received from each cluster node for indexing by one or more indexing applications. The indexed machine data including the complete data set may be stored in one or more index stores. A visualization application enables a user to select one or more analysis lenses that may be used to generate visualizations of the machine data. The visualization application employs the analysis lens to produce visualizations of the computing cluster machine data.

Abstract: Embodiments are directed towards the visualization of machine data received from computing clusters. Embodiments may enable improved analysis of computing cluster performance, error detection, troubleshooting, error prediction, or the like. Individual cluster nodes may generate machine data that includes information and data regarding the operation and status of the cluster node. The machine data is received from each cluster node for indexing by one or more indexing applications. The indexed machine data including the complete data set may be stored in one or more index stores. A visualization application enables a user to select one or more analysis lenses that may be used to generate visualizations of the machine data. The visualization application employs the analysis lens to produce visualizations of the computing cluster machine data.

Abstract: Embodiments are directed towards the visualization of machine data received from computing clusters. Embodiments may enable improved analysis of computing cluster performance, error detection, troubleshooting, error prediction, or the like. Individual cluster nodes may generate machine data that includes information and data regarding the operation and status of the cluster node. The machine data is received from each cluster node for indexing by one or more indexing applications. The indexed machine data including the complete data set may be stored in one or more index stores. A visualization application enables a user to select one or more analysis lenses that may be used to generate visualizations of the machine data. The visualization application employs the analysis lens to produce visualizations of the computing cluster machine data.

Abstract: Embodiments are directed towards the visualization of machine data received from computing clusters. Embodiments may enable improved analysis of computing cluster performance, error detection, troubleshooting, error prediction, or the like. Individual cluster nodes may generate machine data that includes information and data regarding the operation and status of the cluster node. The machine data is received from each cluster node for indexing by one or more indexing applications. The indexed machine data including the complete data set may be stored in one or more index stores. A visualization application enables a user to select one or more analysis lenses that may be used to generate visualizations of the machine data. The visualization application employs the analysis lens to produce visualizations of the computing cluster machine data.

Abstract: Embodiments are directed towards the visualization of machine data received from computing clusters. Embodiments may enable improved analysis of computing cluster performance, error detection, troubleshooting, error prediction, or the like. Individual cluster nodes may generate machine data that includes information and data regarding the operation and status of the cluster node. The machine data is received from each cluster node for indexing by one or more indexing applications. The indexed machine data including the complete data set may be stored in one or more index stores. A visualization application enables a user to select one or more analysis lenses that may be used to generate visualizations of the machine data. The visualization application employs the analysis lens to produce visualizations of the computing cluster machine data.

Abstract: Embodiments are directed towards the visualization of machine data received from computing clusters. Embodiments may enable improved analysis of computing cluster performance, error detection, troubleshooting, error prediction, or the like. Individual cluster nodes may generate machine data that includes information and data regarding the operation and status of the cluster node. The machine data is received from each cluster node for indexing by one or more indexing applications. The indexed machine data including the complete data set may be stored in one or more index stores. A visualization application enables a user to select one or more analysis lenses that may be used to generate visualizations of the machine data. The visualization application employs the analysis lens to produce visualizations of the computing cluster machine data.

Abstract: Embodiments are directed towards the visualization of machine data received from computing clusters. Embodiments may enable improved analysis of computing cluster performance, error detection, troubleshooting, error prediction, or the like. Individual cluster nodes may generate machine data that includes information and data regarding the operation and status of the cluster node. The machine data is received from each cluster node for indexing by one or more indexing applications. The indexed machine data including the complete data set may be stored in one or more index stores. A visualization application enables a user to select one or more analysis lenses that may be used to generate visualizations of the machine data. The visualization application employs the analysis lens to produce visualizations of the computing cluster machine data.

Abstract: A field extraction template simplifies the creation of field extraction rules by providing a user with a set of field names commonly assigned to a certain type of data, as well as guidance on how to extract values for those fields. These field extraction rules, in turn, facilitate access to certain “chunks” of the data, or to information derived from those chunks, through named fields. A field extraction template comprises at least a set of field names and ordering data for the field names. The ordering data indicates index positions that are associated with at least some of the field names. A delimiter is specified for splitting data items into arrays of chunks. The chunk of a data item that belongs to a given field name is the chunk whose position within the item's array of chunks is equivalent to the index position associated with the given field name.

Abstract: Embodiments are directed towards the visualization of machine data received from computing clusters. Embodiments may enable improved analysis of computing cluster performance, error detection, troubleshooting, error prediction, or the like. Individual cluster nodes may generate machine data that includes information and data regarding the operation and status of the cluster node. The machine data is received from each cluster node for indexing by one or more indexing applications. The indexed machine data including the complete data set may be stored in one or more index stores. A visualization application enables a user to select one or more analysis lenses that may be used to generate visualizations of the machine data. The visualization application employs the analysis lens to produce visualizations of the computing cluster machine data.

Abstract: Embodiments are directed towards the visualization of machine data received from computing clusters. Embodiments may enable improved analysis of computing cluster performance, error detection, troubleshooting, error prediction, or the like. Individual cluster nodes may generate machine data that includes information and data regarding the operation and status of the cluster node. The machine data is received from each cluster node for indexing by one or more indexing applications. The indexed machine data including the complete data set may be stored in one or more index stores. A visualization application enables a user to select one or more analysis lenses that may be used to generate visualizations of the machine data. The visualization application employs the analysis lens to produce visualizations of the computing cluster machine data.