1: According to estimates made by IDC, data doubles every two years. T
2: According to estimates made by IDC, data has been growing at a rate of 50% per year. T
3: Big data is a data collection with low value density and high commercial value. T
4: Big data is composed of structured, semi-structured data and unstructured data. T
5: Big data is composed of structured, semi-structured data and unstructured data. F
6: Big data is a data collection with low value density and low commercial value. F
7: Big data is a data collection with high value density and low commercial value. F
8: Hadoop brings together structured and unstructured data. T
9: Hadoop is a large-scale serial processing framework with super computing power. F
10: The CAP principle is that in a distributed system, consistency, availability, and partition fault tolerance cannot be achieved simultaneously. T
11: BASE theory is an extension of CAP theory. The core idea is that even if strong consistency cannot be achieved, the application can use appropriate methods to achieve final consistency. T
12: The storage location of Hive can be freely specified on HDFS when creating the table. T
13: Hive has a special data storage format. F
14: Hive itself has no special data storage format. It basically supports all text formats supported by HDFS. T
15: A Hadoop cluster usually consists of a NameNode and multiple DataNodes. T
16: The ResourceManager in the Hadoop cluster works on the master node and is responsible for unified management and scheduling of the resources of multiple NodeManagers. T
17: In the Hadoop cluster, NodeManager works on the slave node, which is equivalent to the agent of the machine where it is located. It is responsible for local program running, resource management and monitoring. T
18: Hive data is stored in a multi-copy master-slave mode, ensuring data availability. T
19: Hive data is stored in stand-alone mode. F
20: When creating a table in Hive, the user can specify the field separator, newline character, and storage location of the table. T
21: Hive’s partitioning technology can avoid Hive full table scan and improve query efficiency. T
22: Partitions in Hive are subdirectories, which divide a large data set into small data sets according to business needs. T
23: Hive partitioning uses fields outside the table, and Hive bucketing uses fields inside the table. T
24: Hive partitioning uses extra-table fields, and Hive bucketing uses extra-table fields. F
25: Hive partitioning uses in-table fields, and Hive bucketing uses in-table fields. F
26: Hive partitioning uses in-table fields, and Hive bucketing uses in-table fields. F
27: The number of Hive partitions is fixed, and the number of Hive buckets is fixed. F
28: The number of Hive partitions is fixed, but the number of Hive buckets is not fixed. F
29: The number of Hive partitions is fixed, but the number of Hive buckets is not fixed. T
30: The number of Hive partitions and the number of Hive buckets are not fixed. F
31: Hive partitions can be subdivided, and Hive buckets can be subdivided into buckets. F
32: Hive partitions can be subdivided, but Hive buckets cannot be subdivided. T
33: Hive partitions cannot be subdivided, but Hive buckets can be subdivided into buckets. F
34: Hive partitions cannot be subdivided, and Hive buckets cannot be subdivided. F
35: SORT BY in Hive sorts each Reducer internally, but does not sort the global result set. T
36: SORT BY in Hive sorts the global result set. F
37: SORT BY in Hive sorts each Reducer internally and also sorts the global result set. F
38: ORDER BY in Hive can guarantee the total order in the output. T
39: SORT BY in Hive controls the sorting of data in the Reducer. T
40: Use Hive's DISTRIBUTE BY clause to control a specific row to be assigned to a specified Reducer. T
41: In Hive, when the DISTRIBUTE BY and SORT BY fields are the same and the sorting rule is ascending order, you can use the CLUSTER BY method instead. T
42: In Hive, when the DISTRIBUTE BY and SORT BY fields are the same and the sorting rule is descending order, you can use the CLUSTER BY method instead. F
43: In Hive, you can specify sorting rules when using CLUSTER BY to sort fields. F
44: In Hive, descending order can be specified when using CLUSTER BY to sort fields. F
45: Key-value databases are very suitable for operations such as querying and modifying data through keys. T
46: The key-value database has extremely high concurrent read and write performance. T
47: The string of redis can contain any data, such as jpg images or serialized objects. T
48: The Redis list is a simple string list implemented through a linked list, sorted in insertion order. T
49: The elements closer to the head and tail of the Redis list operate faster, and the elements closer to the middle operate slower. T
50: The elements closer to the head of the Redis list operate faster, and the elements closer to the end of the Redis list operate slower. F
51: The elements closer to the end of the Redis list operate faster, and the elements closer to the head operate slower. F
52: A Redis list is an ordered collection. T
53: In the redis database, if data is inserted at the head and tail of the list, the performance will be very high and will not be affected by the length of the linked list. T
54: In the redis database, if data is inserted at the head and tail of the list, performance will be affected by the length of the linked list. F
55: In the HASH type of Redis, the value corresponding to the key is a two-dimensional array. T
56: The Set of redis is an ordered string type collection. F
57: The redis set collection does not allow data duplication. If the added data already exists in the set, only one copy will be retained. T
58: The set type of redis provides aggregation operations between multiple sets, such as intersection, union, and complement. These operations are completed internally in redis and are very efficient. T
59: The ZSet of redis is a string type collection and does not allow duplicate elements. T
60: In redis's ZSet, each element is associated with a double-precision type score. Redis sorts the elements in the set from small to large by the score in the set. T
61: The ZSet of redis is ordered and each element is unique. T
62: The ZSet of redis is ordered, and the set elements can be repeated. F
63: The ordered set of redis is implemented using hash table and skip table. Therefore, compared with lists, operating on intermediate elements is also very fast. T
64: The ordered set type in the Redis data type is more resource-consuming than the list type in the Redis data type. T
65: Data replication is one-way, only from the slave node to the master node. F
66: Data replication is one-way, only from the master node to the slave node. T
67: Data replication is bidirectional, possibly from the master node to the slave node. F
68: In redis, a master node can have multiple slave nodes (or no slave nodes), but a slave node can only have one master node. T
69: In redis, a master node can have multiple slave nodes (or no slave nodes), but a slave node can only have one master node. F
70: HBase is a distributed, column-oriented storage system built on HDFS. T
71: HBase is not a relational database and does not support SQL. T
72: HBase is a relational database, but it does not support SQL. F
73: HBase data is organized using tables. Tables are composed of rows and columns, and columns are divided into several column families. T
74: HBase data is organized using tables. Tables are composed of rows and columns, and columns are divided into several column families. T
75: Data in the HBase column family is located by column qualifiers (or columns). T
76: Each cell in HBase stores multiple versions of the same data, and these versions are indexed by timestamps. T
77: When creating a table in HBase, the column family must be given upfront as part of the schema definition, and column family members can be added later as needed. T
78: When creating a table in HBase, the column family and column family members must be given in advance as part of the schema definition. F
79: When creating a table in HBase, the column family and column family members support dynamic expansion and can be added later as needed. F
80: There will be several row keys in an HBase table, and the values of the row keys cannot be repeated. T
81: HBase row keys are arranged in lexicographic order, with the lowest appearing first in the table. T
82: HBase automatically divides the table into regions horizontally. Each region is composed of several consecutive rows. A region is represented by the three elements of the table it belongs to, the starting row, and the ending row (excluding this row). T
83: Each node (Region Server) in the HBase cluster manages a region of the entire table. F
84: Each node (Region Server) in the HBase cluster manages several regions of the entire table. T
85: When a table has too many rows, HBase will partition the rows in the table based on the value of the row key. T
86: In HBase, different Regions can be distributed on different Region Servers, but a Region will not be split into multiple Region Servers. T
87: In HBase, different Regions can be distributed on different Region Servers, and the same Region can also be split into multiple Region Servers. . F
88: In HBase, different Regions can be placed on the same Region Server, but a Region will not be split into multiple Region Servers. F
89: In HBase, different Regions can be placed on the same Region Server, and a Region can also be split into multiple Region Servers. F
90: HBase Client uses HBase's RPC mechanism to communicate with HMaster and HRegionServer. For management operations, Client performs RPC with HMaster; for data read and write operations, Client performs RPC with HRegionServer. T
91: Zookeeper ensures that there is only one master in the HBase cluster at any time. T
92: HMaster is the implementation of the main server. The main server is responsible for monitoring all RegionServer instances in the cluster and is the interface for all metadata changes. T
93: Hmaster allocates region to Region server. T
94: HBase builds an HStore for each column family. If there are several column families, there will be several HStores. T
95: The HBase region server RegionServer is responsible for storing and maintaining the Region assigned to itself, and responding to the client's read and write requests. T
96: Columns or cells that do not store data in HBase rows do not occupy storage space. The columns corresponding to the column families in a table can change with the business changes of inserting data, and each row and row can be different. T
97: Each row has a sortable primary key and any number of columns. Columns can be dynamically added as needed. Different rows in the same table can have completely different columns. T
98: The data in HBase are all strings and have no type. T
99: The data in each cell of HBase can have multiple versions. T
100: HBase needs to disable the table before it can be deleted. T
101: HBase enabled tables can be deleted. F
102: The main goal of the document database is to build a bridge between key-value storage (providing high performance and high scalability) and traditional relational data systems (rich functions), integrating the advantages of both. T
103: MongoDB is a database based on distributed file storage. T
104: MongoDB database is a collection-oriented and schema-free document type database. T
105: Document databases allow the creation of many different types of unstructured or freely formatted fields. T
106: In MongoDB, within the same collection, the schema of each row of data in the table does not need to be the same. T
107: A collection is a set of MongoDB files. Documents in a collection can have different fields. T
108: The fields of the documents in the MongoDB collection are the same. F
109: When the first MongoDB document is inserted, a non-existing collection is created. T
110: MongoDB documents need to set the same fields, and the same fields need the same data type. F
111: MongoDB documents do not need to set the same fields, and the same fields require the same data type. F
112: MongoDB documents need to set the same fields, but the same fields do not need to have the same data type. F
113: Key/value pairs in MongoDB documents are unordered. F
114: MongoDB documents can have duplicate keys. F
115: When the first MongoDB document is inserted, a collection that does not exist is created. T
116: The selection of the shard key cannot be changed after MongoDB is sharded, and the sharded collection can only have one shard key. T
117: You can change the selection of sharding keys after MongoDB is sharded. A sharded collection can only have one sharding key. F
118: The selection of sharding keys cannot be changed after MongoDB is sharded. A sharded collection can have multiple sharding keys. F
119: After MongoDB is sharded, you can change the selection of sharding keys, and a sharded collection can have multiple sharding keys. F
120: In MongoDB, you don't need to create collections. MongoDB automatically creates collections when you insert some documents. T
121: A graph in a graph database is a collection of vertices and edges. T
122: A graph in a graph database is a collection of vertices and edges. T
123: A graph database is a database that stores pictures. F
124: Relationships in graph databases are undirected. F
125: Property graphs do not allow each node and edge to have a mutable set of property lists. F
126: Graph databases ensure consistency through transactions. T
127: Graph databases allow dangling relationships. F
128: When deleting a node, the graph database can also delete the relationships on it. F
129: Nodes in the attribute graph model contain attributes, but relationships do not. F
130: Both nodes and relationships in the attribute graph model can contain attributes. T
131: A path in a graph database represents a traversal attribute graph, consisting of a series of alternating nodes and relationships. T
132: The node storage area in Neo4j is a fixed-size record storage. T
133: The record size of the contact store in Neo4j is variable. F
134: Attribute records in Neo4j are of fixed size. T
135: To define string type attribute values in Neo4j, you need to use single quotes or double quotes. T
136: UNION clause returns no duplicate data rows in Neo4j. T
137: The UNION ALL clause returns no duplicate data rows in Neo4j. F
138: Big data is a data collection with high value density and high commercial value. F