mwoffliner/lib/util/RedisKvs.d.ts

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import type { RedisClientType } from 'redis';
export default class RedisKvs<T> implements RKVS<T> {
    private redisClient;
    private readonly dbName;
    private readonly dehydratedKeys?;
    private readonly hydratedKeys?;
    constructor(redisClient: RedisClientType, dbName: string, keyMapping?: KVS<string>);
    get(prop: string): Promise<T>;
    getMany(prop: string[]): Promise<KVS<T>>;
    exists(prop: string): Promise<boolean>;
    existsMany(prop: string[], blocking?: boolean): Promise<KVS<boolean>>;
    set(prop: string, val: T): Promise<number>;
    setMany(val: KVS<T>): Promise<number>;
    delete(prop: string): Promise<number>;
    deleteMany(prop: string[]): Promise<number>;
    keys(): Promise<string[]>;
    len(): Promise<number>;
    /**
     * Iteratively call function passed with batches of items from the underlying KVS hash, with a given number of workers
     * working in parallel.
     *
     * This function scans the underlying KVS hash for items to batch. Batch size is "around" 10 items (Redis does
     * not garantee exact batch size for SCAN operation). For every batch retrieved, passed "func" is called with the
     * "items" retrieved.
     *
     * "numWorkers" are started in parallel, each processing a batch of items. Number of "runningWorkers" is passed to
     * "func" called, mostly useful for logging / debugging purposes.
     *
     * This function returns when all items have been batched and passed to processing function.
     */
    iterateItems(numWorkers: number, func: (items: KVS<T>, runningWorkers: number) => Promise<void>): Promise<void>;
    scan(scanCursor: number): Promise<{
        cursor: number;
        items: KVS<T>;
    }>;
    flush(): Promise<number>;
    private hydrateObject;
    private dehydrateObject;
}