![]() ![]() Create the native data file by bulk importing data from SQL Server using the bcp utility. DATAFILETYPE valueįor more information, see Use Character Format to Import or Export Data (SQL Server). Specifies that BULK INSERT performs the import operation using the specified data-file type value. Transact-SQL syntax conventions Syntax BULK INSERT Imports a data file into a database table or view in a user-specified format in SQL Server The only downside of increasing it is that JanusGraph will try for a long time on an unavailable storage backend cluster.Applies to: SQL Server Azure SQL Database Azure SQL Managed Instance ![]() Rule of thumb: Set this value to be as large feasible to not have to wait too long for unrecoverable failures. Important: This value should be the same across all JanusGraph instances.Ģ) ids.renew-timeout configures the number of milliseconds JanusGraph’s id pool manager will wait in total while attempting to acquire a new id block before failing. Rule of thumb: Set this to the sum of the 95th percentile read and write times measured on the storage backend cluster under load. The shorter this time, the more likely it is that an application will fail on a congested storage cluster. There are three configuration options that can be tuned to avoid this.ġ) -time configures the time in milliseconds the id pool manager waits for an id block application to be acknowledged by the storage backend. In addition, the increased write load due to bulk loading may further slow down the process to the point where JanusGraph considers it failed and throws an exception. When id blocks are frequently allocated by many JanusGraph instances in parallel, allocation conflicts between instances will inevitably arise and slow down the allocation process. Hence, be careful to shut down all JanusGraph instances prior to changing this value. Important: All JanusGraph instances MUST be configured with the same value for ids.block-size to ensure proper id allocation. Rule of thumb: Set ids.block-size to the number of vertices you expect to add per JanusGraph instance per hour. Hence, it is generally advisable to increase the block size by a factor of 10 or more depending on the number of vertices to be added per machine. For transactional workloads the default block size is reasonable, but during bulk loading vertices and edges are added much more frequently and in rapid succession. Increasing ids.block-size reduces the number of acquisitions but potentially leaves many ids unassigned and hence wasted. The id block acquisition process is expensive because it needs to guarantee globally unique assignment of blocks. JanusGraph’s id pool manager acquires ids in blocks for a particular JanusGraph instance. Hence, we strongly encourage disabling automatic type creation by setting fault = none in the graph configuration.Įach newly added vertex or edge is assigned a unique id. In particular, concurrent type creation can lead to severe data integrity issues when batch loading is enabled. Important: Enabling storage.batch-loading requires the user to ensure that the loaded data is internally consistent and consistent with any data already in the graph. Now, we can enable storage.batch-loading which significantly reduces the bulk loading time because JanusGraph does not have to check for every added user whether the name already exists in the database. If not, it is simple to sort the profiles by name and filter out duplicates or writing a Hadoop job that does such filtering. If the user profiles are imported from another database, username uniqueness might already guaranteed. ![]() usernames must be unique across the entire graph. Furthermore, assume that the username property key has a unique composite index defined on it, i.e. ![]() The storage.batch-loading configuration option exists because of this observation.įor example, consider the use case of bulk loading existing user profiles into JanusGraph. In many bulk loading scenarios it is significantly cheaper to ensure data consistency prior to loading the data then ensuring data consistency while loading it into the database. In other words, JanusGraph assumes that the data to be loaded into JanusGraph is consistent with the graph and hence disables its own checks in the interest of performance. Enabling batch loading disables JanusGraph internal consistency checks in a number of places. Enabling the storage.batch-loading configuration option will have the biggest positive impact on bulk loading times for most applications. ![]()
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