From 2811ed7b09bda8d5fdb2394c2fc0a869e5890308 Mon Sep 17 00:00:00 2001 From: Alan Bernstein Date: Wed, 7 Mar 2018 16:15:45 -0600 Subject: [PATCH] Improve and linkify glossary --- docs/glossary.md | 48 ++++++++++++++++++++++++------------------------ 1 file changed, 24 insertions(+), 24 deletions(-) diff --git a/docs/glossary.md b/docs/glossary.md index 998bda86b..4607973e9 100644 --- a/docs/glossary.md +++ b/docs/glossary.md @@ -6,50 +6,50 @@ nav = [] ## Glossary -Index: Indexes are the top level container in Pilosa - similar to a database in an RDBMS. Queries cannot operate across multiple indexes. +[Index](../data-model/#index): An Index is a top level container in Pilosa, analogous to a database in an RDBMS. Queries cannot operate across multiple indexes. -Column: Columns are the fundamental horizontal data axis within Pilosa. Columns are global to all Frames within a Index. +[Column](../data-model/#column): Columns are the fundamental horizontal data axis within Pilosa. Columns are global to all [frames](#frame) within an [index](#index). -Row: Rows are the fundamental vertical data axis within Pilosa. They are namespaced to each Frame within a Index. +[Row](../data-model/#row): Rows are the fundamental vertical data axis within Pilosa. They are namespaced to each [frame](#frame) within an [index](#index). Represented as a [bitmap][#bitmap]. -Bit: A bit is the intersection of a Row and Column. +[Bit](../data-model/#overview): Bits are the fundamental unit of data in Pilosa. A bit lives in a [frame](#frame), at the intersection of a [row](#row) and [column](#column). -Bitmap: The on-disk and in-memory representation of a Row. +[Bitmap](../data-model/#overview): The on-disk and in-memory representation of a [row](#row). Implemented with [Roaring](#roaring-bitmap). -Roaring Bitmap: [Roaring Bitmap](http://roaringbitmap.org) is the compressed bitmap format which Pilosa uses. +[Roaring Bitmap](http://roaringbitmap.org): the compressed bitmap format which Pilosa uses to [implement bitmaps](../architecture/#roaring-bitmap-storage-format), for both storage and logical query operations. -Attribute: Attributes can be associated to both rows and columns. This metadata is kept separately from the core binary matrix in a BoltDB store. +[Attribute](../data-model/#attribute): Attributes can be associated to both [rows](#row) and [columns](#column). This metadata is kept separately from the core binary matrix in a [BoltDB](https://github.com/boltdb/bolt) store. -PQL: [Pilosa Query Language](/docs/query-language). +[PQL](../query-language/): Pilosa Query Language. -Frame: Frames are used to segment rows into different categories - row ids are namespaced by frame such that the same row id in a different frame refers to a different row. For Ranked frames, rows are kept in sorted order within the frame. +[Frame](../data-model/#frame): Frames are used to group [rows](#row) into different categories. `RowID`s are namespaced by frame such that the same `RowID` in a different frame refers to a different row. For [ranked](#topn) frames, rows are kept in sorted order within the frame. -View: Views separate the different data layouts within a Frame. The two primary views are Standard and Inverse which represent the typical row/column data and its inverse respectively. Time based Frame Views are automatically generated for each time quantum. Views are internally managed by Pilosa, and never exposed directly via the API. This simplifies the functional interface by separating it from the physical data representation. +[View](../data-model/#view): Views separate the different data layouts within a [Frame](#frame). The two primary views are standard and inverse which represent the typical [row](#row)/[column](#column) data and its inverse respectively (an [inverted index](https://en.wikipedia.org/wiki/Inverted_index), or a matrix transpose). Time based frame views are automatically generated for each time quantum. Views are internally managed by Pilosa, and never exposed directly via the API. This simplifies the functional interface by separating it from the physical data representation. -Fragment: A Fragment is the intersection of a frame and slice in an index. +Fragment: A Fragment is the intersection of a [frame](#frame) and a [slice](#slice) in an [index](#index). -Slice: Columns are sharded on a preset width. Each shard is referred to as a Slice in Pilosa. Slices are operated on in parallel and are evenly distributed across the cluster via a consistent hash. +[Slice](../data-model/#slice): [Columns](#column) are sharded on a preset [width](#slicewidth). Each shard is referred to as a slice in Pilosa. Slices are operated on in parallel and are evenly distributed across the cluster via a [consistent hash](#jump-consistent-hash). -SliceWidth: This is the default number of columns in a slice. +SliceWidth: This is the number of [columns](#column) in a [slice](#slice). By default, 220 or about one million. -MaxSlice: The total number of slices allocated to handle current set of columns. This value is important for all nodes to efficiently distribute queries. +MaxSlice: The total number of [slices](#slice) allocated to handle the current set of [columns](#columns). This value is important for all [nodes](#node) to efficiently distribute queries. -Anti-entropy: A periodic process that compares each slice and its replicas across the cluster to repair inconsistencies. +[Anti-entropy](../configuration/#anti-entropy-interval): A periodic process that compares each [slice](#slice) and its [replicas](#replica) across the [cluster](#cluster) to repair inconsistencies. -Node: An individual running instance of Pilosa server which belongs to a cluster. +Node: An individual running instance of Pilosa server which belongs to a [cluster](#cluster). -Cluster: A cluster consists of one or more nodes which share a cluster configuration. The cluster also defines how data is replicated throughout and how internode communication is coordinated. Pilosa does not have a leader node, all data is evenly distributed, and any node can respond to queries. +Cluster: A cluster consists of one or more [nodes](#node) which share a cluster configuration. The cluster also defines how data is [replicated](#replica) throughout and how internode communication is coordinated. Pilosa does not have a leader node, all data is evenly distributed, and any node can respond to queries. -TopN: Given a Frame and/or RowID this query returns the ordered set of RowID's by the number of columns that have a bit set in that row. +TopN: A [PQL](#pql) query that returns a list of `RowID`s, sorted by the count of [bits](#bit) set in the [row](#row), within a specified [frame](#frame). -Tanimoto: Used for similarity queries on Pilosa data. The Tanimoto Coefficient is the ratio of the intersecting set to the union set as the measure of similarity. +[Tanimoto](../examples/#chemical-similarity-search): Used for similarity queries on Pilosa data. The [Tanimoto Coefficient](https://en.wikipedia.org/wiki/Jaccard_index#Tanimoto_similarity_and_distance) between two [bitmaps](#bitmap) A and B is the ratio of the size of their intersection to the size of their union (|A∩B|/|A∪B|). -Protobuf: [Protocol Buffers](https://developers.google.com/protocol-buffers/) is a binary serialization format which Pilosa uses for internal messages, and can be used by clients as an alternative to JSON. +[Protobuf](https://developers.google.com/protocol-buffers/): Protocol Buffers is a binary serialization format which Pilosa uses for internal messages, and can be used by clients as an alternative to JSON. -TOML: We use [TOML](https://github.com/toml-lang/toml) for our configuration file format. +[TOML](https://github.com/toml-lang/toml): the language used for Pilosa's [configuration file](../configuration). -Jump Consistent Hash: A fast, minimal memory, [consistent hash algorithm](https://arxiv.org/pdf/1406.2294v1.pdf) that evenly distributes the workload even when the number of buckets changes. +[Jump Consistent Hash](https://arxiv.org/pdf/1406.2294v1.pdf): A fast, minimal memory, consistent hash algorithm that evenly distributes the workload even when the number of buckets changes. -Partition: The consistent hash is compiled with a maximum number of partitions or locations on the unit circle that keys are mapped to. Partitions are then evenly mapped to physical nodes. To add nodes to the cluster you simply need to remap the partitions, and associate data across the new cluster topography. +Partition: The [consistent hash](#jump-consistent-hash) maps keys to partitions (or locations on the unit circle), based on a preset maximum number of partitions (256 by default). Partitions are then evenly mapped to physical [nodes](#node). To add nodes to the [cluster](#cluster), the partitions must be remapped, and data is then associated across the new cluster topology. -Replica: A copy of a [fragment](#fragment) on a different host from the original. The "cluster.replicas" configuration parameter determines how many replicas of a fragment exist in the cluster (including the original, so a value of 1 means no extra copies are made). +[Replica](../configuration/#cluster-replicas): A copy of a [fragment](#fragment) on a different [node](#node) than the original. The `cluster.replicas` configuration parameter determines how many replicas of a fragment exist in the cluster. This includes the original, so a value of 1 means no extra copies are made.