Several currently commonly used solutions for metadata management

Metadata is defined as: data describing data, descriptive information about data and information resources.

Metadata is data that describes other data (data about other data), or structured data (structured data) used to provide information about a certain resource. Metadata is data that describes objects such as information resources or data. Its purpose of use is to: identify resources; evaluate resources; track changes in resources during use; realize simple and efficient management of large amounts of networked data; realize effective discovery of information resources, Finding, integrating organization and effective management of used resources.

There are currently several commonly used solutions for metadata management: central node management metadata, distributed management metadata, and metadata-free design; this article talks about the characteristics of the three solutions:

1. Central node management metadata

When designing a distributed (storage) system, using a central node is a very simple and clear solution. The central node usually It has functions such as metadata storage and query, cluster node status management, decision-making and task issuance;

Advantages:

A. Due to its centralized management of metadata, it can be convenient To handle the statistical analysis requirements of cluster operation and maintenance management;

B. The central node records the status information of user data (i.e. metadata). When expanding, you can choose not to perform the rebalance operation (data caused by rebalance Migration may bring huge performance overhead), and can still be addressed normally;

Disadvantages and solutions:

a.Single point of failure is one of the most taboo issues in designing distributed systems , the simple design of the central node also brings this problem. How to implement HA? ; Solution: (1) Use the active-standby model, use synchronous or asynchronous methods for incremental or full data synchronization (such as TFS, mfs, HDFS2.0, etc.), or use remote Shared storage (such as HDFS2.0, remote storage requires high availability);

b. There is an upper limit for performance and capacity expansion, and the centralized central node’s own hardware facilities have an upper limit for expansion (scale up) and query-based addressing. method, leading to this problem; even if the client caches metadata or uses a cache cluster, the upper limit cannot be fundamentally eliminated. In some scenarios (such as massive small files), this problem still exists; Solution: (1) Optimize and upgrade hardware , such as using SSD, large memory and other machines; (2) When faced with this problem, consider using a distributed management metadata solution.

2. Distributed management of metadata

is similar to the central node solution, except that the metadata is fragmented and distributed nodes are used to manage the storage. While retaining the advantages of the central node solution, it solves It solves the problem of upper limit of performance and capacity expansion. At the same time, multiple nodes provide metadata query services at the same time, and the system performance is improved;

Disadvantages

This type of system is relatively rare, and the system itself has a complex structure. It is also difficult to implement;

a. The system contains two relatively independent distributed nodes: metadata nodes and data nodes. They are both stateful nodes. The distributed modules composed of each node must face distribution. The choice of formula CAP principle must be scalable, especially metadata has higher requirements for consistency;

b. Metadata nodes need to jointly maintain the status of data nodes and make decisions when the status changes. Consistent decision-making; these pose great challenges to the design and implementation of the system;

c. In addition, the storage equipment required for a large amount of metadata is also a cost that cannot be ignored;

The above two solutions have the same idea: record and maintain the status of the data (i.e. metadata). When addressing the data, first query the metadata server and then access the actual data;

3. No metadata Design

Mainly takes ceph as an example. Different from the above two ideas, the main idea of this type of system is to use an algorithm to calculate addressing. One of the input parameters of the addressing algorithm is the cluster status (such as data node Some form of description of distribution topology, weight, process status, etc.). Such common algorithms include consistent hashing and the CRUSH algorithm of the Ceph RADOS system. This type of algorithm usually does not directly manage user data, but introduces an intermediate layer of logical sharding structure. (such as the ring fragment of consistent hashing, the placement group of ceph), its granularity is larger, its quantity is limited and relatively fixed, the data accessed by the user belongs to only one of the shards, and the system manages and maintains these shards by managing and maintaining them. User data; some such systems also have central configuration management nodes (such as ceph rados monitor), which only provide management and maintenance of important states such as clusters and shards, and do not provide storage and query of metadata;

Advantages:

A. As mentioned above, the system only needs to manage and maintain information such as logical sharding and cluster status, and does not store and manage metadata of user data. The scalability of the system is greatly enhanced, which is particularly important in scenarios with large amounts of metadata. It is especially obvious when;

B. The amount of parameter data required by the addressing algorithm is small and relatively fixed. The client can achieve the purpose of parallel addressing for several clients through caching, avoiding the addressing performance bottleneck;

Disadvantage analysis:

a. When the cluster is expanded (or even when the weight is changed), rebalance needs to be performed, especially for clusters with large data scale (above PB level). The resulting large amount of data migration will keep the cluster in a high load state. This in turn causes the performance indicators such as latency and iops of normal business requests to decline; however, in some scenarios when performing cluster expansion, rebalance is not desired (for example, the cluster capacity is insufficient). In this regard, a common strategy is to prepare performance and capacity in advance for each cluster. According to the evaluation, when expansion is needed, create a new cluster directly; if a single cluster must be rebalanced, reduce the cluster load through manual intervention and current limiting; as for the fundamental reason for rebalance, I believe that expansion will cause the cluster status to change, which will lead to changes in the addressing algorithm results. The final data distribution also needs to change;

b. The copy distribution position of the data is calculated through the addressing algorithm. The position is relatively fixed and almost cannot be adjusted manually; but the overall data can usually be changed by changing the weight. Distribution;

c. The central configuration management node only manages shard information and does not know the information of individual user data. The requirements for statistical analysis need to be realized by regularly collecting data node information, and storing and maintaining it.

Summary: Through the above comparative analysis, the addressing strategies of the three types of systems make the systems themselves have their own corresponding advantages and disadvantages. They are not perfect, but they all have their suitable scenarios and businesses. In the system When designing and selecting, comprehensive considerations need to be made.