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Indexing and Storage in CrateDB

In this article series, we look at CrateDB from different perspectives. We start from the bottom of CrateDB architecture and gradually move up to higher layers, presenting the most important aspects of CrateDB internals. The motivation is to better understand CrateDB, as well as to aid users in maximizing the effectiveness of CrateDB features.

In the first part, we explore the internal workings of the storage layer in CrateDB. The storage layer ensures that data is stored in a safe and accurate way and returned completely and efficiently. The CrateDB storage layer is based on Lucene indexes. Lucene offers scalable and high-performance indexing which enables efficient search and aggregations over documents and rapid updates to the existing documents. We will look at the three main Lucene structures that are used within CrateDB: Inverted Indexes for text values, BKD-Trees for numeric values, and Doc Values.

Indexing Text Values

The Lucene indexing strategy relies on a data structure called inverted index. An inverted index is defined as a “data structure storing a mapping from content, such as words and numbers, to its location in the database file, document or set of documents“ [Wikipedia]. In Lucene, an index can store an arbitrary size of documents, with an arbitrary number of different fields.

To better explain how inverted indexes are implemented in Lucene, we first introduce Lucene Documents. A Lucene Document is a unit of information for search and indexing that contains a set of fields, where each field has a name and value. Furthermore, each field can be tokenized to create terms. We refer to terms as the smallest units of search and index and they are represented as a combination of a field name with a token. Depending on the analysis, generated terms dictate what type of search we can do efficiently and which not.

Finally, the Lucene index is implemented as a mapping from terms to documents and it is called inverted because it reverses the usual mapping of a document to the terms it contains. The inverted index provides an effective mechanism for scoring search results: if several search terms map to the same document, then that document is likely to be relevant.

Indexing is done before retrieval, and access is done on indexed documents. The major steps in the creation of the Lucene index are illustrated in the following example:

  1. Imagine that we collected two documents to be indexed: “My favorite sweet dish is strawberry cake.“ and “Strawberries are bright red and sweet.“

  2. The next step is the tokenization of text into words: “My“, “favorite“, “sweet“, “dish“, etc.

  3. To produce indexing terms, we use linguistic processing for token normalization. For example, the term “Strawberries“ is normalized to “strawberry“ and the result is used as an indexing term.

  4. Each indexing term is then mapped to document id and the resulting sequence of terms is sorted alphabetically. The instances of the same term are then grouped by word and by document id. The final index contains indexing terms and pointers to the posting lists, i.e., the list of document ids that hold the term.
The diagram below shows the indexing terms from two documents, the sorted sequence, and finally the index.

Sequence-of-terms-Sorted-Sequence-Index

Lucene Segments

A Lucene index is composed of one or more sub-indexes. A sub-index is called a segment, it is immutable and built from a set of documents. When new documents are added to the existing index, they are added to the next segment. Previous segments are never modified. If the number of segments becomes too large, the system may decide to merge some segments and discard the corresponding documents. This way, adding a new document does not require rebuilding the index structure.

Inverted Indexes In CrateDB

CrateDB splits tables into shards and replicas, meaning that tables are divided and distributed across the nodes of a cluster. Each shard in CrateDB is a Lucene index broken into segments and stored on the filesystem. Depending on the configuration of a column the index can be plain (default) or full-text. An index of type plain indexes content of one or more fields without analyzing and tokenizing their values into terms. To create a full-text index, the field value is first analyzed and based on the used analyzer, split into smaller units, such as individual words. A full-text index is then created for each text unit separately.

To illustrate both indexing methods, let’s consider a simple table called Product:

productID name quantity
1 Almond Milk 100
2 Almond Flour 200
3 Milk 300

The inverted index enables a very efficient search over textual data. For our case, it makes sense to index the column “name”. The next two tables illustrate the resulting plain and full-text indexes:

Plain index

name

docID

Almond Milk

1

Almond Flour

2

Milk

3

Fulltext index

name

docID

Almond

1,2

Milk

1,3

Flour

2

There are in total three names in the plain index mapped to different document ids. On the other side, there are three values in the full-text index as a result of column tokenization: in this case, the terms Almond and Milk point to more documents.

Indexing Numeric Values

Until Lucene 6.0 there was no exclusive field type for numeric values, so all value types were simply stored as strings and an inverted index was stored in the Trie-Tree data structure. This type of data structure was very efficient for queries based on terms. However, the problem was that even numeric types were represented as a simple text token. For queries that filter on the numeric range, the efficiency was relatively low. To optimize numeric range queries, Lucene 6.0 adds an implementation of Block KD (BKD) tree data structure.

BKD tree data structure

To better understand the BKD tree, let’s start with a short introduction to KD trees. A KD tree is a binary tree for multidimensional queries. KD tree shares the same properties as binary search trees (BST), but the dimensions alternate for each level of the tree. For instance, starting from the root node, the x value of the left nodes is always less than the x value of the root node. The same applies to the right node and all intermediate nodes up to leaf nodes. KDB tree is a special kind of KD tree with properties found in the B+ trees. This means:

  • KDB tree is a self-balanced tree and can contain more than one dimension

  • In KDB tree data is stored only in leaf nodes, while the intermediate nodes are used as pointers
Finally, BKD trees are composed of several KDB trees. BKD trees provide very efficient space utilization and query performance, regardless of the number of queries.


To construct the KDB tree, we need to choose a dimension as a segmentation criterion. This can be done by calculating the difference range of each dimension and selecting the dimension with the largest difference. Another common selection method is the variance method, where the dimension is chosen based on how large the variance of each dimension is. In the following example, we illustrate the construction of the KDB tree based on the “dimension difference” method.

We start with a total of 8 point data where each point has two dimensions we refer to as x-dimension and y-dimension. The set of points is: {1,2}, {2,8}, {3,4}, {4,3}, {4,6}, {6,7}, {7,11} and {8,9}. Furthermore, we assume that intermediate nodes in the KDB tree can have a maximum of two children. The construction process is as follows:

  • The first segmentation is done on y dimension as (max_x - min_x) < (max_y - min_y) or: 7 < 9. To divide data points we first sort them according to the value of the y dimension. The result after sorting is the following list: {1,2} → {4,3} → {3,4} → {4,6} → {6,7} → {2,8} → {8,9} → {7,11}.

  • Then, we choose the first half of the sorted list as left subtree data and the second half of the list as right subtree data.

  • We continue to segment further the left subtree: now the segmentation criteria is dimension y (4 > 3). However, the segmentation criteria for the right subtree is dimension x (6 > 4). The next splitting is done in the same fashion: the data are sorted and split into left subtree and right subtree data. After this step, each intermediate node has exactly two children and the construction process stops. Finally, the KDB tree is constructed as illustrated in the figure below:

divded-by-y-dimension

The index file with the resulting data structure is then created as a series of blocks that contain data from leaf nodes, intermediate nodes, and the metadata of the BKD tree. The internal representation of index files is beyond the scope of this article.

Range Queries

Numerical indexing relies on BKD-Tree to accelerate the performance of range queries. Considering our KDB tree, to query all points in the range x in [1,8] and y in [9,11], the engine does the following:

  • Starting from the root node we know from the segmentation dimension that all points where y is in [9,11] range are in the right subtree, so the next step is to traverse the right subtree.

  • The next segmentation dimension is the x value and from the segmentation condition, we know that points, where x is in [1,8] range, are in both left and right subtrees. So, we need to traverse both subtrees.

  • All child nodes of the right subtree satisfy our query range and zero child nodes from the left subtree. Finally, the query output is: {7,11} and {8,9}.

Doc Values

Until Lucene 4.0 columns were indexed using an inverted index data structure that maps terms to document ids. For searching documents by terms, this is a very good solution. However, if we have to find field values given document id, this solution was not equally effective. Furthermore, to perform column-oriented retrieval of data, it was necessary to traverse and extract all fields that appear in the collection of documents. This can cause memory and performance issues if we need to extract a large amount of data.

To improve the performance of aggregations and sorting, a new data structure was introduced, namely Doc Values. Doc Values is a column-based data storage built at document index time. They store all field values that are not analyzed as strings in a compact column making it more effective for sorting and aggregations.

CrateDB implements Column Store based on Doc Values in Lucene. The Column Store is created for each field in a document and generated as the following structures for fields in the Product table:

 

Document 1

Document 2

Document 3

productID

1

2

3

name

Almond Milk

Almond Flour

Milk

quantity

100

200

300

For example, for the first document, CrateDB creates the following mappings as Column Store: {productID → 1, name → “Almond Milk“, quantity → 100}.

Column Store significantly improves aggregations and grouping as the data for one column is packed in one place. Instead of traversing each document and fetching values of the field that can also be very scattered, we extract all field data from the existing Column Store. This approach significantly improves the performance of sorting, grouping, and aggregation operations. In CrateDB, Column Store is enabled by default and can be disabled only for text fields, not for other primitive types. Furthermore, CrateDB does not support storing values for container and geographic types in Column Store.

Besides fields, CrateDB also supports Column Store for the JSON representation of each row in a table. For our example, row-based Column Store is generated as the following:

Document

Row

1

{“id“:1, “name“:”Almond Milk”, “quantity“:100}

2

{“id“:2, “name“:”Almond Flour”, “quantity“:200}

3

{“id“:3, “name“:”Milk”, “quantity“:300}

The use of Column Store results in a small disk footprint, thanks to specialized compression algorithms such as delta encoding, bit packing, and GCD.

Summary

This article describes the core design principles of the storage layer in CrateDB. Being based on the Lucene index, it enables effective and efficient search over the arbitrary size of documents with an arbitrary number of fields. Besides inverted indexes, the Lucene indexing strategy also relies on BKD trees and Doc Values that are successfully adopted by CrateDB as well as many popular search engines. With a better understanding of the storage layer, we move to another interesting topic: storage and handling of dynamic objects in CrateDB.

 

 

 

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