Abstract: A system, method, and machine-readable storage medium for performing garbage collection in a distributed storage system are provided. In some embodiments, an efficiency level of a garbage collection process is monitored. The garbage collection process may include removal of one or more data blocks of a set of data blocks that is referenced by a set of content identifiers. The set of slice services and the set of data blocks may reside in a cluster, and a set of probabilistic filters (e.g., Bloom filters) may indicate whether the set of data blocks is in-use. At least one parameter of a probabilistic filter of the set of probabilistic filters may be adjusted (e.g., increased or reduced) if the efficiency level is below the efficiency threshold. Garbage collection may be performed on the set of data blocks in accordance with the set of probabilistic filters.

Abstract: A system, method, and machine-readable storage medium for determining an amount of unique data in a distributed storage system are provided. In some embodiments, a combined efficiency set for a first data set stored in the distributed storage system, such as at a volume, may be generated. The first data set may include a first subset of data and a second subset of data in the distributed storage system. Additionally, a set of efficiency sets for the first subset of data may be generated. A set difference based on the combined efficiency set and the set of efficiency sets may be computed. An amount of memory used for storing unique data of the second subset of data may be estimated based on the set difference. The unique data may be present in the second subset of data but absent from the first subset of data.

Abstract: Systems and methods for sampling a set of block IDs to facilitate estimating an amount of data stored in a data set of a storage system having one or more characteristics are provided. According to an example, metadata (e.g., block headers and block IDs) may be maintained regarding multiple data blocks of the data set. When one or more metrics relating to the data set are desired, an efficiency set, representing a subset of the block IDs of the data set, may be created to facilitate efficient calculation of the metrics by sampling the block IDs of the data set. Finally, the metrics may be estimated based on the efficiency set by analyzing one or more of the metadata (e.g., block headers) and the data contained in the data blocks corresponding to the subset of the block IDs and extrapolating the metrics for the entirety of the data set.

Abstract: A system, method, and machine-readable storage medium for determining an amount of unique data in a distributed storage system are provided. In some embodiments, a combined efficiency set for a first data set stored in the distributed storage system, such as at a volume, may be generated. The first data set may include a first subset of data and a second subset of data in the distributed storage system. Additionally, a set of efficiency sets for the first subset of data may be generated. A set difference based on the combined efficiency set and the set of efficiency sets may be computed. An amount of memory used for storing unique data of the second subset of data may be estimated based on the set difference. The unique data may be present in the second subset of data but absent from the first subset of data.

Abstract: Systems and methods for sampling a set of block IDs to facilitate estimating an amount of data stored in a data set of a storage system having one or more characteristics are provided. According to an example, metadata (e.g., block headers and block IDs) may be maintained regarding multiple data blocks of the data set. When one or more metrics relating to the data set are desired, an efficiency set, representing a subset of the block IDs of the data set, may be created to facilitate efficient calculation of the metrics by statistically sampling the block IDs of the data set. Finally, the metrics may be estimated based on the efficiency set by analyzing one or more of the metadata (e.g., block headers) and the data contained in the data blocks corresponding to the subset of the block IDs and extrapolating the metrics for the entirety of the data set.

Abstract: A system, method, and machine-readable storage medium for performing garbage collection in a distributed storage system are provided. In some embodiments, an efficiency level of a garbage collection process is monitored. The garbage collection process may include removal of one or more data blocks of a set of data blocks that is referenced by a set of content identifiers. The set of slice services and the set of data blocks may reside in a cluster, and a set of probabilistic filters (e.g., Bloom filters) may indicate whether the set of data blocks is in-use. At least one parameter of a probabilistic filter of the set of probabilistic filters may be adjusted (e.g., increased or reduced) if the efficiency level is below the efficiency threshold. Garbage collection may be performed on the set of data blocks in accordance with the set of probabilistic filters.

Abstract: Systems and methods for sampling a set of block IDs to facilitate estimating an amount of data stored in a data set of a storage system having one or more characteristics are provided. According to an example, metadata (e.g., block headers and block IDs) may be maintained regarding multiple data blocks of the data set. When one or more metrics relating to the data set are desired, an efficiency set, representing a subset of the block IDs of the data set, may be created to facilitate efficient calculation of the metrics by statistically sampling the block IDs of the data set. Finally, the metrics may be estimated based on the efficiency set by analyzing one or more of the metadata (e.g., block headers) and the data contained in the data blocks corresponding to the subset of the block IDs and extrapolating the metrics for the entirety of the data set.

Abstract: A system, method, and machine-readable storage medium for performing garbage collection in a distributed storage system are provided. In some embodiments, an efficiency level of a garbage collection process is monitored. The garbage collection process may include removal of one or more data blocks of a set of data blocks that is referenced by a set of content identifiers. The set of slice services and the set of data blocks may reside in a cluster, and a set of filters may indicate whether the set of data blocks is in-use. At least one parameter of a filter of the set of filters may be adjusted (e.g., increased or reduced) if the efficiency level is below the efficiency threshold. Garbage collection may be performed on the set of data blocks in accordance with the set of filters.

Abstract: A system, method, and machine-readable storage medium for determining an amount of unique data in a distributed storage system are provided. In some embodiments, a combined efficiency set for a first data set stored in the distributed storage system, such as at a volume, may be generated. The first data set may include a first subset of data and a second subset of data in the distributed storage system. Additionally, a set of efficiency sets for the first subset of data may be generated. A set difference based on the combined efficiency set and the set of efficiency sets may be computed. An amount of memory used for storing unique data of the second subset of data may be estimated based on the set difference. The unique data may be present in the second subset of data but absent from the first subset of data.

Abstract: A system, method, and machine-readable storage medium for determining an amount of unique data in a distributed storage system are provided. In some embodiments, a combined efficiency set for a first data set stored in the distributed storage system, such as at a volume, may be generated. The first data set may include a first subset of data and a second subset of data in the distributed storage system. Additionally, a set of efficiency sets for the first subset of data may be generated. A set difference based on the combined efficiency set and the set of efficiency sets may be computed. An amount of memory used for storing unique data of the second subset of data may be estimated based on the set difference. The unique data may be present in the second subset of data but absent from the first subset of data.

Abstract: A system, method, and machine-readable storage medium for performing garbage collection in a distributed storage system are provided. In some embodiments, an efficiency level of a garbage collection process is monitored. The garbage collection process may include removal of one or more data blocks of a set of data blocks that is referenced by a set of content identifiers. The set of slice services and the set of data blocks may reside in a cluster, and a set of filters may indicate whether the set of data blocks is in-use. At least one parameter of a filter of the set of filters may be adjusted (e.g., increased or reduced) if the efficiency level is below the efficiency threshold. Garbage collection may be performed on the set of data blocks in accordance with the set of filters.

Abstract: Disclosed are systems, computer-readable mediums, and methods for efficiency sets in a distributed system. A first efficiency set is determined for a first volume of data. Determining the first efficiency set includes selecting block identifiers for data blocks of the first volume, where each block identifier is used to access a particular data block corresponding to the first volume. Determining the first efficiency set further includes applying a mask to the selected block identifiers to mask at least one bit of each selected block identifier. The first efficiency set is compared to a second efficiency set for a second data store, and based on the comparison, an amount of unique data blocks of the first volume is approximated.

Abstract: Disclosed are systems, computer-readable mediums, and methods for efficiency sets in a distributed system. A first efficiency set is determined for a first volume of data. Determining the first efficiency set includes selecting block identifiers for data blocks of the first volume, where each block identifier is used to access a particular data block corresponding to the first volume. Determining the first efficiency set further includes applying a mask to the selected block identifiers to mask at least one bit of each selected block identifier. The first efficiency set is compared to a second efficiency set for a second data store, and based on the comparison, an amount of unique data blocks of the first volume is approximated.

Abstract: Disclosed are systems, computer-readable mediums, and methods for efficiency sets in a distributed system. A first efficiency set is determined for a first volume of data. Determining the first efficiency set includes selecting block identifiers for data blocks of the first volume, where each block identifier is used to access a particular data block corresponding to the first volume. Determining the first efficiency set further includes applying a mask to the selected block identifiers to mask at least one bit of each selected block identifier. The first efficiency set is compared to a second efficiency set for a second data store, and based on the comparison, an amount of unique data blocks of the first volume is approximated.

Abstract: Disclosed are systems, computer-readable mediums, and methods for efficiency sets in a distributed system. A first efficiency set is determined for a first volume of data. Determining the first efficiency set includes selecting block identifiers for data blocks of the first volume, where each block identifier is used to access a particular data block corresponding to the first volume. Determining the first efficiency set further includes applying a mask to the selected block identifiers to mask at least one bit of each selected block identifier. The first efficiency set is compared to a second efficiency set for a second data store, and based on the comparison, an amount of unique data blocks of the first volume is approximated.