Axibase Time Series Database

Transcription

1 Axibase Time Series Database

2 Axibase Time Series Database Axibase Time-Series Database (ATSD) is a clustered non-relational database for the storage of various information coming out of the IT infrastructure. ATSD is specifically designed to store and analyze large amounts of statistical data collected at high frequency. 2 Prepared by Axibase

3 Database History 1970 IBM introduced relational algebra for data processing. Cambrian explosion of relational database management systems: 2000 first large-scale applications emerge, such as Google Search Google Big Table first non-relational database using distributed file system. Currently we are experiencing Cambrian explosion of non-relational (a.k.a. NoSQL) databases: 3 Prepared by Axibase

4 Key Differences Between SQL and NoSQL SQL NoSQL High-level Programming Language SQL Transactions Query Optimizer Non-key indexes 4 Prepared by Axibase

5 Key Differences Between SQL and NoSQL SQL NoSQL Scalability TB PB Maximum Cluster Size 48 (Oracle RAC) Distributed Read Time Write Time Table Schema (column names, data types) Depends on table size and indexes Depends on table size and indexes Predetermined Linear Linear Raw bytes. Schema determined by application 5 Prepared by Axibase

6 How Proven Is NoSQL Technology NoSQL is the leading technology behind big data applications. Google search, gmail, AppEngine Yahoo/Microsoft search Amazon e-commerce, search, cloud computing (AWS DynamoDB) IBM Big Insights, Microsoft Azure HD Insight 6 Prepared by Axibase

7 Big Data Adoption HBase behind Facebook Messages: 6+ billion messages per day 75+ billion R/W operations per day Peak throughput: 1.5 million R/W operations per second 2+ petabytes of data (6+ PB including replicas) with data growth of over 8 TB per day 7 Prepared by Axibase

8 Big Data Adoption IBM BigInsights behind Vestas: A wind energy company in Denmark is reducing the time to analyze petabytes of data from several weeks to 15 minutes to improve the accuracy of wind turbine placement. Stores 2.8 PB of company historical data together with over 178 external parameters: temperature, barometric pressure, humidity, precipitation, wind direction, wind velocity etc. Stores precise data on weather over the past 11 years. Collects data from over 35,000 meteorological stations. 8 Prepared by Axibase

9 Big Data Adoption HBase behind Explorys: Explorys uses HBase to enable search and analysis of patient populations, treatment protocols, and clinical outcomes. Stores over 275 billion clinical, financial and operational data elements. 48 million unique patient files. Collecting data from over 340 hospitals and 300,000 healthcare providers. Pull data from 22 integrated major healthcare systems. 9 Prepared by Axibase

10 Axibase Time Series Database Scalability & Speed Collects billions of samples per day. Retains detailed data forever. Features Combines database, rule engine, and visualization in one product. Analytical Rule Engine Applies aggregate functions and filters on streaming data. Integration Accepts data from any source based on industry-standard protocols. Visualization Built-in portals with smart widgets. 10 Prepared by Axibase

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12 Big Data for IT Monitoring Retain detailed data forever. Collect statistics at high-frequency, for example every 15 seconds. Consolidate performance statistics from all systems into one database: facilities, network, storage, servers, applications, databases, transactions, service providers, user activity etc. Monitor infrastructure based on abnormal deviations instead of manual thresholds. Apply statistical formulas to predict outages. Take advantage of schema-less database to collect data from any source. 12 Prepared by Axibase

13 Big Data for Developers Support for annotation-style instrumentation. Alternative to byte-code instrumentation and file logging. Collect detailed performance and usage statistics for reporting and analytics, without writing custom monitors. 13 Prepared by Axibase

14 Big Data for Operations Gather and analyze statistical data generated by the various systems and sensors. Analytics that can support decision control systems. Allows for better real time operations decision support. Generate accurate forecasts of upcoming issues: Delays Scheduled maintenance based on product usage and sensor data instead of warranty periods Improved customer service times and standards. 14 Prepared by Axibase

15 ATSD Architecture ATSD architecture combines database, analytics and reporting tools into one complete product. Data locality makes analytics run faster. Application server layer is simplified to provide core shared services 15 Prepared by Axibase

16 ATSD Components Pluggable driver provides support for different storage engines Compute, persistence and data collection layers scaled independently 16 Prepared by Axibase

17 Fault Tolerance ATSD is a distributed system, with high fault tolerance. Each data sample is automatically replicated 3 times for recovery. 17 Prepared by Axibase

18 ATSD Scalability ATSD is a distributed, non-relational database with high throughput, fault tolerance and reading speed. ATSD can collect billions of metrics per day and store petabytes of data. ATSD supports millisecond resolution and sampling intervals of up to several measurements per second. The data is stored without losing accuracy. Additional nodes can be added at runtime to handle increasing volumes. ATSD automatically distributes the table across active nodes. New nodes can be added in remote data centers to minimize network traffic. 18 Prepared by Axibase

19 Supported Data Types Two types of data ingestion: push and pull. ATSD supports numeric values, log messages and properties (collection of key-values). ATSD uses collectors for retrieving structured and unstructured data from remote sources. Support for standard protocols: Telnet, ICMP, CSV/TSV, FILE, JMX, HTTP, and JSON. 19 Prepared by Axibase

20 Data Collection Collection is agentless; data is pushed by external systems into ATSD. New metrics are auto-registered. No need to update schema or restart any server components. Existing monitoring tools can be instrumented to stream data into ATSD. Each data sample can be tagged (key = value) at source for subsequent querying, aggregations, and roll-ups. 20 Prepared by Axibase

21 Data Storage Built-in data compression provides 70%-80% disk space savings over raw data. No data needs to be deleted. Seek time is almost linear regardless of the dataset size. Data storage is sparse and efficient. ATSD stores only what is collected instead of long rows with NULLs or zeros, as is the case in relational model. VMware VMFS-attached disks are sufficient for small to medium clusters. Direct attached disks with JBOD are recommended for larger clusters. JBOD alternatives to minimize node recovery time are available from leading storage vendors, such as NetApp E-Series. 21 Prepared by Axibase

22 Built-in Instruments Unlike conventional data warehouses, ATSD comes with a set of built-in tools for data analysis: Analytical Rule Engine Forecasting Visualization 22 Prepared by Axibase

23 Analytical Rule Engine Evaluates incoming data in memory based on statistical rules. Statistical rules are applied to the incoming data stream before data is stored on disk. As data is ingested by ATSD server, a subset of samples that match rule queries are routed to the rule engine for processing. Rule Engine supports both time- and count- based data windows. Rule expressions and filters can reference not just numeric values but also tags such as system type, location, priority to ensure that alerts are raised only for critical issues. Multiple metrics and entities can be correlated within the same rule. 23 Prepared by Axibase

24 Analytical Rule Engine Rule Examples Type Window Example Description threshold none value > 75 Raise an alert if last metric value exceeds threshold range none value > 50 AND value <= 75 Raise an alert if value is outside of specified range statistical-count count(10) avg(value) > 75 Raise an alert if average value of the last 10 samples exceeds threshold statistical-time time('15 min') avg(value) > 75 Raise an alert if average value for the last 15 minutes exceeds threshold statistical-deviation time('15 min') avg(value) / avg(value(time: '1 hour')) > Raise an alert if 15-minute average exceeds 1-hour average by more than 25% 1.25 statistical-ungrouped time('15 min') avg(value) > 75 Raise an alert if 15-minute average values for all entities in the group exceeds threshold metric correlation time('15 min') avg(value) > 75 AND avg(value(metric: 'loadavg.1m')) > 0.5 Raise an alert if average values for two separate metrics for the last 15 minutes exceed predefined thresholds entity correlation time('15 min') avg(value) > 75 AND avg(value(entity: 'host2')) > 75 Raise an alert if average values for two entities for the last 15 minutes exceed thresholds threshold override time('15 min') avg(value) >= entity.grouptag('cpu Raise an alert if 15-minute average value exceeds minimum threshold specified for groups to which _avg').min() the entity belongs cpu forecast deviation time('5 min') abs(forecast_deviation(wavg())) > 2 Raise an alert if 5-minute average deviates from forecast by more than two standard deviations cpu forecast diff time('10 min') abs(wavg() - forecast()) > 25 Raise alert if absolute forecast deviates from average by more than specified value disk threshold time('15 min') new_maximum() && threshold_linear_time(99) < 120 Raise alert if last value is the highest observed and linear threshold is expected to violate the 99% threshold in less than 120 minutes 24 Prepared by Axibase

25 Analytical Rule Engine 25 Prepared by Axibase

26 Analytical Rule Engine 26 Prepared by Axibase

27 Forecasting Customers have a growing need to predict problems before they occur. The accuracy of predictions and the percentage of false positives/negatives highly depends on the frequency of data collection, the retention interval, and algorithms. The use of built-in autoregressive time-series extrapolation algorithms (Holt-Winters, ARIMA, etc.) in ATSD allows predicting of system failures at early stages. The forecasting process is resource intensive and is most effective in a clustered system with data locality such as ATSD. Dynamic predictions eliminate the need to set manual thresholds. 27 Prepared by Axibase

28 Forecasting Example 28 Prepared by Axibase

29 Forecasting Example 29 Prepared by Axibase

30 Forecast Settings ATSD selects the most accurate forecasting algorithm for each time-series separately based on a ranking system. The winning algorithm is used to compute forecast for the next day, week or month. Pre-computed forecasts can be used in rule engine. 30 Prepared by Axibase

31 Forecast Settings 31 Prepared by Axibase

32 Visualization ATSD can be integrated with Axibase Enterprise Reporting using the ATSD adapter ATSD comes with a wide variety of widgets for creating interactive portals directly in ATSD. ATSD widgets are designed from the ground-up to handle large data sets and calculations on the client. ATSD visualization is supported on mobile devices and Smart TVs. 32 Prepared by Axibase

33 Visualization 33 Prepared by Axibase

34 Search Implemented in ATSD is log file search system to detect problems in distributed systems for the purposes of security, audit and change control. Notifications Supports standard notification mechanisms: , console, web service, and notification in the environment. For example, Axibase LED lighting system - the "Data Cube", which changes colors depending on the status of IT services. 34 Prepared by Axibase

35 ATSD Benefits Enables customers to extract value from data that already exists in their operational and IT infrastructures. Delivers preemptive monitoring through identification of abnormal behaviors in production systems. Eliminates most manually-defined rules from the customer s monitoring catalog. Serves as a centralized repository for historical data. Directly supported by AER for Dashboards, Reports, Capacity Planning 35 Prepared by Axibase

36 System Requirements Operating Systems: Red Hat Enterprise Linux 5.6+ Ubuntu Suse Linux Enterprise Server 10+ Computing Hardware: Edition Community - FREE Standard Enterprise ATSD Nodes > 5 Processors 2 vcpu, 2+ GHz 4 vcpu, 2+ GHz 4 vcpu, 2+ GHz Memory 4 GB (2GB for JVM) 16 GB (8GB for JVM) 16 GB (8GB for JVM) 36 Prepared by Axibase

37 Use Cases ITM long-term history extension nmon reporting for AIX, Linux and Solaris Minimize exceptions in monitoring catalog Collect environmental data from SCADA Predictive Maintenance based on sensors 37 Prepared by Axibase

38 ITM History Extension ITM can be instrumented to write streaming data into CSV files. CSV can be instantly uploaded into ATSD using inotify utility and wget. Example: private history streaming in ITM KHD_CSV_OUTPUT_ACTIVATE = Y 38 Prepared by Axibase

39 ITM History Extension Warehouse Proxy Agent is setup to save history data to CSV file on the local machine. ATSD ingests the CSV files for analytics and long-term storage. ATSD converts the data using built in parsers. 39 Prepared by Axibase

40 nmon Reporting Consolidate trusted statistics from UNIX systems in one database ATSD is able to collect, parse and analyze nmon files Analyze nmon data with forecasting algorithms Capitalize on nmon data with two predefined visualization portals or easily create your own portals using built-in HTML5 widgets 40 Prepared by Axibase

41 nmon Predefined Portals 41 Prepared by Axibase

42 Predefined AIX Portal 42 Prepared by Axibase

43 Predefined Linux Portal 43 Prepared by Axibase

44 Contact Axibase Axibase Contact Details: General Fax sales@axibase.com Our headquarters are located in Cupertino, Silicon Valley: Stevens Creek Blvd. Cupertino, CA USA 44 Prepared by Axibase